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Evaluation of the Relationship Between Aspartate Aminotransferase/Alanine Aminotransferase Ratio and Coronary Slow-Flow Phenomenon
No full textAim: To assess the correlation between the ratio of aspartate aminotransferase (AST) and alanine aminotransferase (ALT) and slow coronary flow (SCF). Methods: A total of 119 patients with SCF and 121 age- and sex-matched patients with normal coronary flow were included in this retrospective cross-sectional study. Thrombolysis in myocardial infarction frame count of angiography images was marked by two interventionalists. In addition to AST and ALT levels, complete blood count; kidney, liver and thyroid function tests; age; sex; medication and medical history were recorded. Results: Significant spikes in AST/ALT ratio were discovered in the SCF group compared with the normal coronary flow group (p 0.001). Thrombolysis in myocardial infarction frame count values were significantly higher in all coronaries for the SCF group compared with the normal coronary flow group (p 0.001). Thrombolysis in myocardial infarction frame count values of the left anterior descending artery (r = 0.258; p 0.001), left circumflex artery (r = 0.297; p 0.001) and right coronary artery (r = 0.195; p = 0.002) were positively correlated with AST/ALT ratio. Conclusion: AST/ALT ratio is significantly correlated with SCF; therefore, this ratio may have predictive value
Investigation of a Non-Linear Cramér-Lundberg Risk Model
No full textIn this study, a non-linear version of a Cramér-Lundberg risk model is examined. The objective of this work is to evaluate the ruin probability of a non-linear risk model. The classical linear Cramér-Lundberg model has been widely studied in the literature. However, the linear model is not always realistic. Because an insurance company's premium income cannot always increase linearly. Therefore, it is recommended to adapt premium income as a function which increases monotonically and yet its rate of growth decreases over time. Thus, to account for this, a more realistic non-linear mathematical model has been constructed and investigated, when the premium income function is p(t)=c?t. Then Lundberg type upper bound was calculated for the ruin probability for the model under investigation
Can the Generalizability Problem of Artificial Intelligence Be Overcome?: Pneumothorax Detection Algorithm
No full textProportional and Conservativity: the View From Turkish
No full textProportional numerals like 70% and two-thirds, which express a relation between two quantities, give rise to distinct interpretations inside different constructions. In The company hired 70% of the women, the cardinality of the women that are hired is compared to the cardinality of all the women (a proportional reading). In The company hired 70% women, on the other hand, the cardinality of the women that are hired is compared to the cardinality of all the hired individuals (a reverse-proportional reading). This paper is concerned with the form-meaning relations associated with proportional measurement constructions and reverse proportional measurement constructions in Turkish. Proportional numerals inside reverse proportional expressions appear to violate a semantic universal known as the Conservativity Constraint. Building on previous research, we propose a Conservativity-compatible analysis for such constructions in Turkish. We claim that these expressions exhibit non-conservativity effects due to the fact that the noun inside them is interpreted only in its vP-internal position. Proportional measurement constructions, on the other hand, undergo DP-movement, as a result of which the noun inside them is interpreted both in the tail and in the head of the movement chain, blocking non-conservativity effects. We show that the analysis developed in this paper for proportional expressions also accounts for the distribution of non-conservative readings of the proportional context-sensitive determiners many and few
A Topology Optimization Methodology With Vibration Constraint for an Aerospace Bracket Design
No full textASME International Mechanical Engineering Congress and Exposition (IMECE) -- OCT 30-NOV 03 -- 2022 -- Columbus -- OHThe most important need in the aviation industry is the realization of high-strength and lightweight designs. For this reason, topology optimization methods have become widespread recently. Besides, meeting the natural frequency requirements is one of the important design elements. However, topology optimization with stiffness maximization requires a static finite element analysis evaluation while the natural frequency calculation requires a modal analysis evaluation. Using these two different analysis procedures at the same time in the topology optimization process, on the other hand, is a challenging task. To address this challenge, a topology optimization methodology that accounts for the natural frequency constraint in a compliance minimization process is presented in this study. Since the commercial software can either minimize compliance or minimize the vibration frequency at one time, using these two different analysis procedures at the same time together stands out as an innovative aspect of this study. The applicability of the developed methodology is shown for two bracket designs; namely, the so-called GE bracket and a real-world satellite bracket with natural frequency and mass constraints. The prototypes of the designs are fabricated using the additive manufacturing technique.The authors acknowledge the funding provided by Turkish Aerospace Industries for this study.Amer Soc Mech EngineersTurkish Aerospace Industrie
Etkin Osteointegrasyon Amaçlı Alümina Membran ile Kaplanmış Titanyum İmplantların Üretilmesi
No full textİmplant uygulamalarında osteointegrasyonun etkin olarak sağlanması, implant yüzeylerinin kemik ile benzer kimyasal, mekanik ve topografik özellikleri taşıyan yapıda olmasına bağlıdır. Titanyum temelli malzemeler bu amaçla klinikte sıklıkla tercih edilmekte ve hücre-malzeme etkileşimini tetikleyici kimyasal/nanotopografik özellikler sağlayabilen oksit tabakalar ile kaplanabilmektedir. Projemizde geniş alanlı deformasyona sahip kraniyofasiyal benzeri implant uygulamalarındaki güncel yüksek başarısızlık oranlarını gidermek amacı ile titanyum üzerine kaplanan oksit tabakalarla kemik-implant entegrasyonunun artırılarak revizyon ameliyatı gerektirmeyen uzun ömürlü implantların eldesi hedeflenmiştir. Bu amaçla, Ti alttaşlar üzerine kaplanan ince bir Al tabakanın farklı elektrolitlerde anodizasyonu ile nanogözenekli AAO ile kaplanmış Ti implantlar elde edilmiş ve hücre tutunumu, farklılaşması ve mineralizasyonunu tetikleyebilecek kimyasal ve topografik ögeler içeren, görece kolay elde edilebilen biyoaktif bir yüzey kaplaması oluşturulmuştur. SEM ve AFM ile morfolojik; XPS ve XRD ile kimyasal; kayma, çekme ve sertlik testleri ile mekanik karakterizasyonları gerçekleştirilen yüzeylerin hücre kültürü deneylerinde MTT tuzu ile canlılık, floresan boyama ile tutunum ve alizarin boyaması ile farklılaşma analizleri yapılmıştır. Hücrelerin osteojenik aktiviteleri ise literatürde ilk defa projemiz kapsamında qRT-PCR ile gerçekleştirilmiş olup protein düzeyindeki aktivitenin incelenmesinde Western blotting denemelerinin yanı sıra immünositokimya analizi yapılmıştır. Ayrıca konformal bir kaplama tekniği olan atomik tabaka kaplama (ALD) yöntemi ile bu yüzeyler saf alümina ile kaplanmış ve bu şekilde iyonik içeriği baskılanmış ALD-kaplı nanogözenekli oksit tabakalar oluşturularak benzer hücre çalışmaları gerçekleştirilmiş ve AAO tabaka kaynaklı iyonik karakterin hücreler üzerindeki etkisini ortaya çıkarmak amaçlanmıştır. Son olarak membran topografisinin hücre davranışlarına olan etkisinin anlaşılabilmesi için ALD tekniği ile düz Ti implantlar kaplanmış, elde edilen düz gözeneksiz alümina tabakalarda da hücre çalışmaları yapılarak nanogözenekli yüzeylerle kıyaslanmıştır. Bu çalışmalar sonucunda etkin osteointegrasyon potansiyeli olan ve implant uygulamalarında kullanımı ön plana çıkabilecek en iyi yüzeyin fosforik asit içerisinde üretilip iyonik kimliği baskılanan yüzey olabileceği görülmüştür
A Digital Twin Framework for Mechanical Testing Powered by Machine Learning
No full textASME 2022 International Mechanical Engineering Congress and Exposition, IMECE 2022 -- 30 October 2022 through 3 November 2022 -- 186577The aviation industry demands innovation in new materials and processes which can demonstrate high performance with minimum weight. Strength-to-weight ratio (STR) is the key metric that drives the value justification in this demand stream. However, aviation's test and certification procedures are time-consuming, expensive, and heavily regulated. This study proposes a Digital Twin (DT) framework to address the time and high costs of mechanical testing procedures in the aviation industry. The proposed DT utilizes new Machine Learning (ML) techniques such as Transfer Learning (TL). Hence, a proof-of-concept study using TL in the Aluminum material group has been demonstrated. The promising results revealed that it was possible to reduce the test load of new material to 40% without any significant error. Copyright © 2022 by ASME.The authors are thankful for the valuable advice of Assoc.Prof. Ersin Bahceci throughout this study
Prediction of Uterine Dehiscence Via Machine Learning by Using Lower Uterine Segment Thickness and Clinical Features
No full textDoganay, Melike/0000-0002-2603-1812; Kement, Mervenur/0000-0002-4806-5642; Kement, Cihan Emre/0000-0003-0525-351XBACKGROUND With the global increase of cesarean delivery rates, the long-term effects of cesarean delivery have started to become clearer. One of the most prominent complications of cesarean delivery in recurrent pregnancies is uterine rupture. Assessing the risk of uterine rupture by accurately predicting dehiscence is very important to prevent untimely operations and/or maternal and fetal complications. OBJECTIVE This study aimed to assess whether machine learning can be used to predict uterine dehiscence by using patients' ultrasonographic findings, clinical findings, and demographic data as features. Hence, we investigated a potential method for preventing uterine rupture and its maternal and fetal complications. STUDY DESIGN The study was conducted on 317 patients with term (>37 weeks' gestation) singleton pregnancies and no medical complications or medications that may affect uterine wound healing. Demographics, body mass indices, smoking and drinking habits, clinical features, past pregnancies, number and history of abortions, interdelivery period, gestational week, number of previous cesarean deliveries, fetal presentation, fetal weight, tocography data, transabdominal ultrasonographic measurement of lower uterine segment full thickness and myometrium thickness, and lower uterine segment findings during cesarean delivery were collected and analyzed using machine learning techniques. Logistic regression, multilayer perceptron, support vector machine, random forest, and naive Bayes algorithms were used for classification. The dataset was evaluated using 10-fold cross-validation. Correct classification rate, F-score, Matthews correlation coefficient, precision-recall curve area, and receiver operating characteristic area were used as performance metrics. RESULTS Among the machine learning techniques tested in this study, the naive Bayes algorithm showed the best predictive performance. Among the various combinations of features used for prediction, the essential features of parity, gravidity, tocographic contraction, cervical dilation, dilation and curettage, and sonographic thickness of lower uterine segment myometrium yielded the best results. The second-best performance was achieved with sonographic full thickness of lower uterine segment added to the base features. The base features alone could classify patients with 90.5% accuracy, whereas adding the myometrium measurement increased the classification performance by 5.1% to 95.6%. Adding the full thickness measurement to the base features raised the classification performance by 4.8% to 95.3% in terms of correct classification rate. CONCLUSION The naive Bayes algorithm can correctly classify uterine dehiscence with a correct classification rate of 0.953, an F-score of 0.952, and a Matthews correlation coefficient value of 0.641. This result can be interpreted as indicating that by using clinical features and lower uterine segment ultrasonography findings, machine learning can be used to accurately predict uterine dehiscence