OBJECTIVE:Symphyseal disjunction is a rare event in childbirth, occurring almost exclusively after vaginal delivery. Radiologically, it is defined by a symphyseal gap greater than 10 mm. No comparative studies have been done on the potential risk factors involved in its development. The aim of the present study was to describe the circumstances of occurrence and risk factors for symphyseal disjunction. METHODS:This was an exploratory case-control study (1:4). Cases were from literature since January 2010 to December 2023 were joined with disjunctions observed in our center between April 2021 and April 2023. Controls were randomly selected from vaginal deliveries in our center during the same period. RESULTS:A total of 20 cases were observed (12 in the literature, 8 in our center). Pain was constant and gait disturbance frequent (85%). Disjunctions were mostly diagnosed a few days after birth (median 3.5 [1; 5.5] [extremes 0.5-90 days]). Surgical osteosynthesis was required in one quarter of the cases. Comparison with controls showed that neither parity, neonatal weight, instrumental delivery nor delivery conditions were associated with the occurrence of these disjunctions. On the other hand, neonatal head circumference at birth was significantly higher in the disjunction group (35 [34; 36] vs. 33 [32; 34] cm, P < 0.001). Significantly higher values of biparietal diameter were also observed in cases of disjunction compared to controls (84.1 [82.0; 86.1] vs. 81.4 [80.0; 82.9] mm, P = 0.005). CONCLUSION:Symphyseal disjunction is a rare event most often presenting with pain and difficulty in walking after vaginal delivery. Its occurrence seems to be related to the fetal head circumference, and not to the conditions of vaginal delivery.
Abstract This article introduces new one-parameter discrete trigonometric distributions for analyzing count data. In particular, it focuses on a simple, one-parameter discrete trigonometric version of the Lindley distribution, known as the discrete Sin-Lindley distribution. The key mathematical properties are derived, including the probability mass function, cumulative distribution function, quantile function, probability generating function, moments, skewness, kurtosis, and order statistics. The maximum likelihood approach is then employed to estimate the unique parameter. Simulation studies demonstrate the effectiveness of the new model across varying sample sizes. The applicability and robustness of the model are demonstrated by analyzing five real-world datasets and by comparing it with Lindley-related distributions and discrete trigonometric distributions, highlighting its potential in statistical analysis.
Graphene, a remarkable material with extraordinary properties, has revolutionary potential for various technological applications. However, conventional methods of graphene production often involve energy-intensive and polluting processes. To address this challenge, we propose a sustainable approach that combines the production of high-quality graphene from recycled battery waste and its integration into nanofluids. Thanks to a simple and scalable electrochemical exfoliation technique, it is possible to obtain graphene with superior properties. The present study focuses on the large-scale preparation of graphene by recycling graphite from energy storage devices using a simple and inexpensive electrochemical technique. Characterization of the obtained materials was carried out by Raman spectroscopy, X-ray diffraction, specific surface area analysis, and scanning electron microscopy. The viscosity measurement and analysis of a graphene–water nanofluid were carried out at different temperatures and volume fractions. All viscosity measurements were carried out using a capillary viscometer at temperatures between 25 and 65 °C. The nanofluid showed increasing viscosity with increasing nanoparticle concentration and decreasing viscosity with increasing temperature.
