A neural network based traffic-flow prediction model

Prediction of traffic-flow in Istanbul has been a great concern for planners of the city. Istanbul as being one of the most crowded cities in the Europe has a rural population of more than 10 million. The related transportation agencies in Istanbul continuously collect data through many ways thanks to improvements in sensor technology and communication systems which allow to more closely monitor the condition of the city transportation system. Since monitoring alone cannot improve the safety or efficiency of the system, those agencies actively inform the drivers continuously through various media including television broadcasts, internet, and electronic display boards on many locations on the roads. Currently, the human expertise is employed to judge traffic-flow on the roads to inform the public. There is no reliance on past data and human experts give opinions only on the present condition without much idea on what will be the likely events in the next hours. Historical events such as school-timings, holidays and other periodic events cannot be utilized for judging the future traffic-flows. This paper makes a preliminary attempt to change scenario by using artificial neural networks (ANNs) to model the past historical data. It aims at the prediction of the traffic volume based on the historical data in each major junction in the city. ANNs have given very encouraging results with the suggested approach explained in the paper. © Association for Scientific Research

Tibial rotation assessment using Artificial Neural Networks

Assessment of the tibial rotations by the conventional approaches is generally difficult. An investigation has been made in this study to assess the tibial motions based on the prediction of the effects of physical factors as well as a portion of tibial measurements by making use of Artificial Neural Networks (ANN). Therefore, this study aimed at the prediction of the relations between several physical factors and tibial motion measurements in terms of Artificial Neural Networks. These factors include gender, age, weight, and height. Data collected for 484 healthy subjects have been analyzed by Artificial Neural Networks. Promising results showed that the ANN has been found to be appropriate for modeling and simulation in the data assessments. The paper gives detailed results regarding the use of ANN for modeling tibial rotations in terms of physical factors. The study shows the feasibility of ANN to predict the behaviour of knee joints. © Association for Scientific Research

Assessment of the Level of Knowledge about Migraines and Medication Among Pharmacy Technicians

Objective: A limited number of studies focus on pharmacy technicians (PT) and their knowledge level regarding migraines. The present study aimed to determine their level of awareness and knowledge regarding migraines and migraine treatment. Materials and Methods: This cross-sectional study was conducted at Erciyes University Faculty of Medicine in Kayseri, Türkiye. It was conducted from February 2019 to May 2019 and completed with the participation of 324 PTs (75.8% response rate). Forms containing questions about the diagnosis of migraines, the characteristics, and the treatments were administered to the PTs in person and then evaluated by a neurologist. Results: The ratio of PTs who knew of a drug therapy that reduced migraine attacks was 10.2%. Most PTs (85.2%) had no training in migraine treatment and obtained knowledge from their work experience. Ninety-one participants (28.1%) recommended medication to patients who visited the pharmacy due to headaches. When asked about the drug they recommend for migraines, 29.6% suggested ergotamine, 26.9% suggested analgesics, and 15.7% suggested triptans. Conclusion: The results of this study revealed that PTs working in pharmacies that support primary care services might not have sufficient awareness and knowledge about migraine treatments, and appropriate training should be provided on this subject

Changes in electrocardiographic p wave parameters after cryoballoon ablation and their association with atrial fibrillation recurrence

Background: Changes in P wave parameters after circumferential pulmonary vein isolation (CPVI) have been previously identified. In this study, we aimed to determine the changes in P wave parameters surface electrocardiogram (ECG) after cryoballoon ablation (CBA) for atrial fibrillation (AF) and evaluate their relationship with AF recurrence. Methods: Sixty-one patients (mean age 53 ± 11 years, 50.8% male) with paroxysmal AF who underwent CBA were enrolled. A surface ECG was obtained from all patients immediately before the procedure, and repeated 12 hours after the procedure. P wave amplitude (Pamp), P wave duration (Pwd), and P wave dispersion (Pdis) values in preprocedural and postprocedural ECGs were measured and compared. Recurrence rates of AF in 3, 6, and 9 months following ablation were recorded for all patients. Changes in P wave parameters were compared between patients with and without AF recurrence. Results: Compared to preprocedural measurements, Pamp (from 0.58 ± 0.18 mV at baseline to 0.48 ± 0.17 mV, P 0.05). Conclusion: Pamp, Pwd, and Pdis parameters exhibited significant decrease after CBA compared to preprocedural measurements. Decreased Pamp was shown to be a predictor for good clinical outcomes following CBA

Impact of clinicopathological variables on laparoscopic hysterectomy complications, a tertiary center experience

Objectives: To analyze intraoperative and postoperative complications according to Clavian-Dindo Classification (CDC) and evaluate the influence of clinicopathological features on the feasibility and safety of total laparoscopic hysterectomy (TLH) in patients that underwent surgery in a tertiary center. Material and methods: We retrospectively reviewed the database of 469 patients that underwent surgery for patients who underwent extra facial TLH from 2013 to 2020. Results: A total of 86 (18.3%) peri-postoperative complications were observed. The incidence of intraoperative complications was 2% (n = 10). The overall conversion rate to open surgery was 1.9% (n = 9). A total of 76 postoperative complications were observed in 61 patients (14.3%). The incidence of minor (Grade I [n = 16, 3.4%] and II [n = 42, 8.9%]) and major complications (Grade III [n = 15, 3.2%], IV [n = 2, 0.4%] and V [n = 1, 0.2 %]) were 12.3% and 3.8%, respectively. A higher BMI and performing surgery at the first step of learning are found to be associated with intraoperative and postoperative complications (p < 0.05). Postoperative complications related to having a history of the cesarean section, additional comorbidities, and uterine weight ≥ 300 g (p < 0.05). Conclusions: The implementation of TLH by experienced surgeons appears to have remarkable advantages over open surgery. However, the risk factor for complications should be taken into account by surgeons in the learning curve in selecting the appropriate patient for surgery.