Search

Patients suffering from headache, particularly migraine type, are among the most dissatisfied patients. The aim of this study was comparing the efficacy of pregabalin with valproate sodium, in preventing migraine headache. In a randomized, double-blinded study, adult patients eligible for prophylactic treatment (i.e., patients with 4-15 attacks per month in last two months) were recruited. Patients' demographic data, duration of symptoms, headache frequency (attacks per month) and intensity (based on visual analogue scale) and also drugs used to relief headache were recorded. The patients were randomly assigned to two groups; valproate sodium (200 mg two times daily) and pregabalin (50 mg two times daily). The patients were examined by neurology specialist monthly for three months and the related data were recorded. The Data were analyzed using SPSS version 21, with related statistical tests. Total number of 140 patients with recurrent migraine were entered into the study. Sixty-nine patients were assigned to group A and 71 to group B by the randomizing table. Inter-group analysis of data in two arms of the study showed that two medications were equally effective except that pregabalin was not significantly effective in reducing number of attacks during first month of therapy compared to baseline. This differences were not significant at second and third month of the study. Our study showed that pregabalin, has comparable efficacy with valproate sodium in reducing migraine frequency, intensity, and duration of attacks and could be an alternative for migraine prophylaxis. [ABSTRACT FROM AUTHOR]

This study investigates the effect of activating metacognitive strategies on the listening performance of English as a foreign language (EFL) university students and explores the impact of such strategies on their metacognitive awareness of the listening task. The participants wereN= 50 students of English literature at the state university of Qom, Iran. After screening the participants from among 60 students, they were randomly assigned to the experimental and control groups. The experimental group (n= 25) received the metacognitive strategy instruction based on the models proposed by Vandergrift and Tafaghodtari (2010), while the control group (n= 25) received just the listening input with no strategy instruction. The listening module of the International English Language Testing System (IELTS) was utilized to evaluate the listening performance of the participants in both groups in pretests and posttests, and the Metacognitive Awareness Listening Questionnaire (MALQ) instrument was applied to measure the metacognitive awareness of the treatment group before and after the treatment. The results of the IELTS test revealed that the experimental group significantly outperformed the control group on the posttest and according to the analysis of the MALQ instrument there was a significant improvement in the students’ level of metacognitive awareness after strategy instruction. The interview results in the discussion section also supported the findings and shed more light on the details. [ABSTRACT FROM AUTHOR]

Experimental and numerical studies were performed to examine the relationship between the hydraulic properties and physical dimensions of a subsurface weir system and the discharge over it under steady-state conditions. Experimental modelling was based on a laboratory-scale physical sand-tank model, within which a subsurface dam and weir structure was constructed. To evaluate the effect of the dominant parameters on the discharge coefficient, numerical modelling was also carried out. A number of physical and numerical models of subsurface dams and weirs were constructed and the effect of each parameter on the discharge coefficient was studied. The results indicate that the hydraulic conductivity of the aquifer, the head above the weir and the effective width of the aquifer, in addition to the geometry of the subsurface weir, significantly affect discharge efficiency. It was also shown that the Reynolds and Weber numbers only have a small effect on the discharge coefficient. Based on the experimental results, a threshold value is suggested for the subsurface weir submergence. Finally, according to the results of both experimental and numerical models, an equation is derived to estimate the discharge coefficient of subsurface weirs. [ABSTRACT FROM AUTHOR]

Introduction: Bell's palsy is a sudden unilateral paralysis of the facial nerve. Postoperative Bell's palsy following surgery is rare. It occurs in less than 1% of operations. The hypothesis: We premise that the main cause of immediate postoperative Bell's palsy is latent herpes viruses (herpes simplex virus type 1 and herpes zoster virus), which are reactivated from cranial nerve ganglia. Inflammation of the nerve initially results in a reversible neurapraxia, but ultimately Wallerian degeneration ensues. The palsy is often sudden in onset and evolves rapidly, with maximal facial weakness developing within two days. Associated symptoms often seen in idiopathic Bell's palsy are tearing problems, hyperacusis and altered taste. Evaluation of the hypothesis: Facial paralysis presenting postoperatively is distressing and poses a diagnostic challenge. A complete interruption of the facial nerve at the stylomastoid foramen paralyzes all the muscles of facial expression. Taste sensation may be lost unilaterally and hyeracusis may be present. Idiopathic Bell's palsy is due to inflammation of the facial nerve in the facial canal. Bell's palsy may also occur from lesions that invade the temporal bone (carotid body, cholesteatoma, dermoid cyst, acoustic neuromas). Although traumatic Bell's palsy cannot be ruled out, it seems logic to postulate that the main cause of immediate postoperative Bell's palsy is latent herpes viruses. [ABSTRACT FROM AUTHOR]

Objective: Sex reassignment surgery is accepted in properly planned cases to change facial appearance in accordance with a new gender. Although changing the hairdo or makeup, and depilation, give satisfying cosmetic results in many patients, hard and soft tissue facial surgeries are needed in most cases. Unfortunately, few studies on facial corrections have been published and feminizing a male face is still considered an arbitrary undertaking. We present a useful surgical protocol to feminize the male face. Method: Ten male-to-female transsexuals aged 20 to 32 years (average 23 years) were referred after gender reassigning for facial feminization (1990-2007). Clinical examination of the patients revealed variable degrees of common masculine features such as long square faces, broad chins, excessive projection, frontal bossing, prominent flaring gonial angles, high hairlines, and low-set eyebrows. After complete clinical assessment and paraclinical workup including cephalometry, the patients were admitted for feminizing surgery. The face was outlined in geometric proportions. It was aimed to round the male face using a sequence of staged surgical procedures for both hard and soft tissues. Results: Ten male patients aged 20 to 32 years (average 23 years) underwent facial feminization using basic surgical guidelines. Various amounts of soft and hard tissue changes were required for individual patients and were tailored for each case according to the preoperative workup and standard parameters. Conclusion: All 10 patients were satisfied with their improvement in appearance; the degree of impact these procedures may have on lifestyle remains to be assessed. [ABSTRACT FROM AUTHOR]

• A prediction methodology to predict wind and solar power curtailments in CAISO. • Machine learning-based forecasting using cross-validation and hold-out approaches. • Analyzing the six-year hourly data of power plants, power exchange, and demand. • A correlation matrix of eight input features and the three target variables. • Cross-validation improved the prediction of oversupply using random forest method. The economic viability of renewable energy is deteriorating due to its curtailment in power systems. Therefore, it is imperative to forecast curtailments for more effective utilization. To alleviate this issue, in this paper, we propose artificial intelligent-based models to accurately predict wind and solar power curtailments (WSPCs), which have not been investigated before. In this regard, a prediction methodology is developed using different types of machine learning (ML) methods and evaluated based on both hold-out (HO) and cross-validation (CV) approaches. The ML methods considered include regression trees (RT), gradient boosting trees (GBT), random forest (RF), feed-forward artificial neural networks (ANN), long short-term memory (LSTM), and support vector machines (SVR). The prediction models are trained based on eight input features, including load demands, the output power of thermal power plants, nuclear units, solar farms, wind turbines, biomass/geothermal units, large hydro units, power imports, and WSPC as two target variables. Based on historical data, i.e., hourly records of California independent system operator (ISO), the predictive models are validated, and the optimal hyperparameters are chosen using Bayesian optimization for each model to attain the best results. Among all the models, the RF model results in the minimum prediction errors and thus the best performance by implementing the proposed CV approach. The obtained results demonstrate the effectiveness of the proposed models in the prediction of WSPCs. [ABSTRACT FROM AUTHOR]