Insight into soft chemometric computational learning for modelling oily-wastewater separation efficiency and permeate flux of polypyrrole-decorated ceramic-polymeric membranes.

Reliable modeling of oily wastewater emphasizes the paramount importance of sustainable and health-conscious wastewater management practices, which directly aligns with the Sustainable Development Goals (SDG) while also meeting the guidelines of the World Health Organization (WHO). This research explores the efficiency of utilizing polypyrrole-coated ceramic-polymeric membranes to model oily wastewater separation efficiency (SE) and permeate flux (PF) based on established experimental procedures. In this area, computational simulation still needs to be explored. The study developed predictive regression models, including robust linear regression (RLR), stepwise linear regression (SWR) and linear regression (LR) for the ceramic-polymeric porous membrane, aiming to interpret its complex performance across diverse conditions and, thus, develop its utility in oily wastewater treatment applications. Subsequently, a novel, simple average ensemble paradigm was explored to reduce errors and improve prediction skills. Prior to the development of the model, stability and reliability analysis of the data was conducted based on Philip Perron tests with the Bartlett kernel estimation method. The accuracy of the SE exhibited a high consistency, averaging 99.92% with minimal variability (standard deviation of 0.026%), potentially simplifying its prediction compared to PF. The modes were validated and evaluated using metrics like MAE, RMSE, Speed, and MSE, in addition to 2D graphical and cumulative distribution function graphs. The LR model emerged as the best with the lowest RMSE =0.21951, indicating superior prediction accuracy, followed closely by RLR with an RMSE = 0.22359. SWLR, while having the highest RMSE = 0.34573, marked its dominance in prediction speed with 110 observations per second. Notably, the RLR model justified a reduction in error by approximately 35.29% compared to SWLR. Moreover, the training efficiency of the LR model exceeded, demanding a mere 2.9252 s, marking a reduction of about 32.54% compared to SWLR. The improved simple ensemble learning proved merit over the three models regarding error accuracy. This study emphasizes the essential role of soft-computing learning in optimizing the design and performance of ceramic-polymeric membranes.

Vagal Paraganglioma: A Rare Finding in a 31-Year-Old Male.

Vagal paraganglioma is a rare finding that develops from paraganglionic tissue found around the vagus nerve; it has a prevalence of 0.012% of all tumors. It is the third most common paraganglioma of the head and neck but still accounts for less than 5% of these tumors, and it has a well-established female prevalence. It is a difficult tumor to identify early based on its symptoms alone and only a thorough investigation can help solidify its diagnosis. In this report, we discuss a presentation of this phenomenon that is not only unique in its manifestation but also a very difficult diagnosis due to its deceptive presentation and multiple extensions. These masses need a good surgical regime to be removed properly and postoperative complications are very frequent in most of these cases.

A Rare Case of Homocysteinemia Presenting With Multiple Aneurysms in an Adolescent Boy.

Thoracoabdominal aortic aneurysm (TAAA) is primarily a disorder of the elderly; the condition, however, is rare in children, for whom the misdiagnosis is not uncommon. It is one of the leading causes of death in the older age group worldwide, with a 4:1 male to female ratio. There are no real data about the incidence of aortic aneurysms in childhood. Although rare, an aortic aneurysm can be an important cause of mortality in children and adolescents. We present a case of an adolescent boy with a left coronary artery aneurysm, left axillary artery aneurysm, and TAAA type-IV caused by the metabolic disease homocysteinemia. He was referred to our facility when the complicated picture of the disease was discovered.

Evaluation of heart rate variability, blood pressure and lipid profile alterations from dual transceiver mobile phone radiation exposure.

OBJECTIVES: Electromagnetic fields have been reported to alter electrical activities in the brain and heart. However, there is paucity of information on the potential functional alterations that magnetic fields from mobile phone could cause to the heart. This study investigated heart rate variability (HRV), blood pressure (BP) and lipid profile in Wistar rats exposed to electromagnetic field radiation from a dual transceiver mobile phone (DTrMP). METHODS: Twenty-one male albino Wistar rats (140-180 g) were randomly assigned to two major groups positioned 5 m apart as follows: control: no phone (n=7) and treatment group (n=14) continuously exposed to electromagnetic field from Tecno T312 DTrMP 900/1800 MHz set in silence mode. Experimental treatment consisted in 10 min calls/day, directed to this device for a period of six weeks. Seven animals from the treatment group were allowed to recover for a period of two weeks after exposure. HRV, systolic, diastolic and mean arterial BP were noninvasively investigated, while serum lipid profile and heart tissue nitric oxide (NO) activities were determined using standard procedures. RESULTS: There was significant (p<0.05) increase in systolic, diastolic, mean arterial BP and a decrease in HRV. Serum high density lipoproteins decreased, while total cholesterol, atherogenic indices, and heart NO levels increased significantly in the radiation exposed animals. The alterations observed in exposed animals remained unchanged even after the recovery period. CONCLUSIONS: These results suggest that exposure to electromagnetic radiation from dual transceiver mobile phones could be a risk factor to increase in blood pressure.