Introduction: Globally, unsafe abortion is a significant cause of maternal mortality and morbidity. One of the commonest problems facing university and college students is unwanted pregnancy followed by abortion. This study has aimed to assess abortion practice of university and college female students and to identify contributing factors.
Methodology: Cross-sectional study design was used in 2011. Female students from one university and three colleges of Arba Minch town were selected by proportional probability sampling method. Quantitative data were collected using a self-administered structured questionnaire and focus group discussions were also conducted.
Results And Discussion: Eight hundred and thirteen study participants with median age 20 have been involved in the study. Among participants 173 (21.3%) had had sex, 54 (6.6%) had been pregnant, and out of the students who had been pregnant 23 (43.4%) had an induced abortion, 4 (17.3%) of which were done under unsafe conditions. Students' current living residence and knowledge of abortion law are the identified contributing factors to their abortion practices.
Conclusion: A significant proportion of pregnancies in university and college students were terminated with induced abortion. Unsafe sex is the commonest cause of unplanned pregnancy that leads to abortion induction. Campus residents are more vulnerable to abortion induction. Knowledge of abortion law and abortion induction practices are statistically interrelated.
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http://dx.doi.org/10.1016/j.srhc.2013.12.001 | DOI Listing |
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December 2024
College of Economics and Management, Huazhong Agricultural University, Wuhan, 430070, Hubei, China.
In light of the Chinese government's dual carbon goals, achieving cleaner production activities has become a central focus, with regional environmental collaborative governance, including the management of agricultural carbon reduction, emerging as a mainstream approach. This study examines 268 prefecture-level cities in China, measuring the carbon emission efficiency of city agriculture from 2001 to 2022. By integrating social network analysis and a modified gravity model, the study reveals the characteristics of the spatial association network of city agricultural carbon emission efficiency in China.
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December 2024
Department of Electrical and Electronics Engineering, Engineering Faculty, Düzce University, Düzce, Turkey.
The study suggests a better multi-objective optimization method called 2-Archive Multi-Objective Cuckoo Search (MOCS2arc). It is then used to improve eight classical truss structures and six ZDT test functions. The optimization aims to minimize both mass and compliance simultaneously.
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December 2024
Merchant Marine College, Shanghai Maritime University, Shanghai, 201306, China.
The intelligent identification of wear particles in ferrography is a critical bottleneck that hampers the development and widespread adoption of ferrography technology. To address challenges such as false detection, missed detection of small wear particles, difficulty in distinguishing overlapping and similar abrasions, and handling complex image backgrounds, this paper proposes an algorithm called TCBGY-Net for detecting wear particles in ferrography images. The proposed TCBGY-Net uses YOLOv5s as the backbone network, which is enhanced with several advanced modules to improve detection performance.
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December 2024
The School of Nursing, Fujian Medical University, No. 1 Xuefu North Road, Fuzhou, 350122, Fujian, China.
Diabetes Mellitus combined with Mild Cognitive Impairment (DM-MCI) is a high incidence disease among the elderly. Patients with DM-MCI have considerably higher risk of dementia, whose daily self-care and life management (i.e.
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December 2024
College of Information Engineering, SuQian University, SuQian, 223800, China.
The safety and reliability of rotating machinery hinge significantly on the proper functioning of rolling bearings. In the last few years, there have been significant advances in the algorithms for intelligent fault diagnosis of bearings. However, the vibration signals collected by machines are inevitably affected by irrelevant noise because of the complex working environments of bearings.
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