During the month of Ramadan, practicing Muslims abstain from eating and drinking from sunrise to sunset. We aimed to investigate the effect of Ramadan fasting on arousal and continuous attention. The electrodermal activity and cancellation test of students were measured in fasting and non-fasting conditions after the conclusion of the Ramadan fast period. The skin conductance level of the fasting group was no different from the non-fasting group. In non-fasting group, the skin conductance response amplitude to an auditory stimulus was higher and the skin conductance response onset latency was lower than in the fasting group. Cancellation test results: the fasting group had a lower total number of marked targets (TNTM) but a higher total number of missed targets (TNMT) and length of time for the subject to complete the test (LTCT) than the non-fasting group. Ramadan fasting did not change arousal, but the reaction time to an auditory stimulus increased during the Ramadan intermittent fasting. Both reaction amplitude and continuous attention also decreased in the fasting condition.
The utility of the pleth variability index in predicting anesthesia-induced hypotension in geriatric patients Abstract Background/aim: Anesthesia induced hypotension may have negative consequences in geriatric patients. Therefore, predicting hypotension remains an important topic for anesthesiologists. Pleth Variability Index (PVI) measurement provides information about the fluid status and vascular tonus of the patients. In our study, the ability of Pleth Variability Index to predict hypotension after general anesthesia induction was evaluated. Materials and methods: PVI values obtained from pulse oximetry were recorded in addition to preoperative standard anesthesia monitoring. The correlation between the PVI value and mean arterial pressure (MAP), systolic arterial blood pressure (SAP) changes and the power of PVI values to predict the incidence of hypotension after anesthesia induction (>20% MAP decrease) was tested. Results: Eighty patients over 65 years of age who were operated under general anesthesia were included in the study. Hypotension was observed in 20 patients (25%). PVI values were mild and positively correlated with MAP changes (r = 0.195 and p=0.041). According to the ROC analysis, the incidence of hypotension increased in patients with PVI values above 15.45%. We also found the following diagnostic results for PVI value for predicting hypotension: p=0.044 and Area Under ROC Curve 0.651 ± 0.073 (95%, Confidence Interval: CI 0.507-0.794), Sensitivity of 40%, specificity of 80%, PPV of 40%, NPV of 80%, cutoff value of 15.45, positive likehood ratio of 2, negative likehood ratio of 0.75, Youden Index of 0.2. 2 Conclusion: Predicting hypotension in geriatric patients is an important issue for anesthesiologists. As an easily applicable test, The Pleth Variability Index is useful in predicting MAP reduction in patients. This practical technique can be used routinely in all geriatric patient groups.
Abstract. The fabric defect detection has crucial importance in terms of sectoral quality. As fabric defection stage, accordingly the growing market volume and production capacity, detection via human vision has caused largely time-wasting and success rate decreasing until 60%. Due to a fabric has unique texture, there is necessity for it to work on separately from other images types while extracting its features. Features are vital material of computer vision especially classification problems. Hence, extracting right features is the most significant stage of error detection. This purpose in mind on this study, deep learning which distinguishes with multi-layer architectures and reveals high achievement on image and speech procession recent years by self-feature extraction is applied to fabric defect detection. Stacked autoencoder -a deep learning method-that aimed to represent input data via compression or decompression is tried to detect defect of fabrics and it gained acceptable success. The principal aim of this study is to increase achievement of feature extraction by tuning up the input value and hyper parameters autoencoder. Thanks to the fine tuning of hyper-parameters of deep model, we have 96% success rate on our own dataset. Keywords: Deep learning, fabric defect detection, autoencoder, hyper parameter Kumaş Hatası Tespiti için Yığınlanmış Oto-kodlayıcı YöntemiÖzet. Kumaş hatası tespiti sektörel kalite açısından önem arz etmektedir. Bu hataların tespitinde, gelişen pazar hacmi ve üretim kapasitelerinin büyüklüğü sebebiyle insan görüsü ile tespit, büyük oranda zaman kaybına ve hata tespit oranının %60 seviyelerine kadar düşmesine sebep olmaktadır. Bu bağlamda daha yüksek başarım elde edebilmek için görüntü işleme alanında bir çok yöntem denenmiştir. Kumaşın kendine has bir dokusunun olması sebebiyle, öznitelikleri çıkarılırken diğer görüntü türlerinden ayrı olarak incelenmesi gerektirmektedir. Öznitelikler bilgisayarlı görmede özellikle sınıflandırma problemlerinde ham madde olmaktadır. Bu yüzden doğru öznitelikleri çıkarmak, hata tespitinde en önemli aşamadır. Bu amaç doğrultusunda, çoklu-katman mimarisi ve kendi özniteliklerini çıkararak son yıllarda görüntü ve ses işleme alanında büyük başarılar getirmesi ile öne çıkan derin öğrenme kumaş hatası tespitine uygulanmıştır. Giriş verisini sıkıştırma ya da genişletme ile temsil eden yığınlı oto-kodlayıcılar -bir derin öğrenme yöntemi-kumaş hatası tespitinde denenmiş ve kabul edilebilir başarılar elde edilmiştir. Çalışmanın asıl amacı oto kodlayıcının hiper parametreleri ve giriş değeri ile oynamalar yaparak öznitelik çıkarımı başarısını artırmaktır. Derin modelin hiper parametrelerin ince ayarıyla, kendi veri setimizde %96'lık bir başarı oranı elde ettik.Anahtar Kelimeler: Derin öğrenme, kumaş hatası tespiti, oto-kodlayıcı, hiper parametre
Introduction: Spinal anesthesia (SA) is one of the most frequently applied anesthesia procedures today. However, SA failure rate varies between 1 and 17%. The age of the patient, the position at which the procedure is performed, or the characteristics of the technical operation can affect success. In this study, we aimed to compare the most frequent SA failures according to the types of surgery and causes of failure. The results of SA procedures performed in a university hospital were compare to those published in the current literature. Materials and Methods: After obtaining ethics committee approval for our study, the hospital archives were examined retrospectively for 1 year with respect to SA procedures. SA application and failure rates were examined. Three or more SA attempts, failed dural puncture, or unsuccessful injection, and anesthesia applications that did not provide sufficient sensory block for surgery despite successful drug treatment were defined as failure. Results: Of all anesthesia procedures, SA was applied at a rate of 23.5%. Our SA failure rate was calculated as 16.6%. Considering a single surgical procedure, obstetric anesthesia was the most common surgery with failed SA (28.7%). The most common cause of failure was insufficient analgesia (32.9%). Discussion: SA failure rates were observed to be in a variable distribution range in the literature, and in some studies, SA failure was defined as a block that did not occur despite a full dose and successful injection, and this rate was found to be 3.9%. The high rate in our study group may be explained by differences in the definition of SA: blocks performed with several trials and any block that could not be applied were also recorded as SA failure. The reasons for failing to apply this procedure is an issue that is worth examining also in terms of patient satisfaction and safety, which is an important issue. Conclusion: Although the definition of unsuccessful SA is confusing, SA failure rates are worth examining and improving for each hospital.
scite is a Brooklyn-based startup that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
334 Leonard St
Brooklyn, NY 11211
Copyright © 2023 scite Inc. All rights reserved.
Made with 💙 for researchers