Development of Early Stage Diabetes Prediction Model Based on Stacking Approach

Diabetes is a disease that may pose direct or indirect risks in terms of human health. Early diagnosis can minimize the potential harm of this disease to the body and reduce the probability of death. For this reason, laboratory tests are performed on diabetic patients. The analysis of these tests enables the diagnosis of diabetes. The aim of this study is so quickly diagnose diabetes by using data obtained from patients with machine learning methods. In order to diagnose the disease, k-nearest neighbor (k-NN), logistic regression (LR), random forest (RF) models and the stacking meta model which is created by combining these three models were used. The dataset used in the research includes test samples taken from 520 people. The dataset has 17 features, including 16 input features and 1 output feature. As a result of the classification through this dataset, different classification results were obtained from the models. The classification success of the models LR, k-NN, RF and stacking were found to be 91.3%, 91.7%, 97.9% and 99.6%, respectively. F-score, precision and recall performance metrics were utilized for a detailed analysis of the models\u27 classification results. The obtained results revealed that the stacking model has a sufficient level to be used as a decision support system in the early diagnosis of diabetes

Chronic Inducible Urticaria: Part II

Physical urticaria (PU) is a subgroup of acquired, chronic inducible urticaria which is associated with a known physical trigger. In PU, the symptoms are induced by exogenous physical triggers, such as friction, pressure, vibration, cold, heat, or solar radiation. All the PUs may manifest with both wheals and angioedema at the sites of the triggers with the exceptions that urticaria factitia (UF) (symptomatic dermatographism) presents with wheals only and pressure urticaria presents with angioedema only. More than one form of physically induced urticarias can be present in one patient

Dermatitis Herpetiformis

Dermatitis herpetiformis is an autoimmune skin disease, which is strongly related to coeliac disease. Moreover, some authors accept it as the skin manifestation of coeliac disease. It is a chronic, recurrent disease with polymorphic skin eruptions and pruritus. Dermatitis herpetiformis is a disease of the young adults mostly, but can be seen at any age. It is characterized by papules, vesicles, excoriations, and urticarial plaques clinically. Histopathological examination reveals subepidermal separation, and with this finding, it needs to be differentiated from linear IgA bullous dermatitis and bullous pemphigoid. In this case, direct immunofluorescence is helpful. Granular deposition of IgA is pathognomonic for dermatitis herpetiformis. Dermatitis herpetiformis can accompany other autoimmune disorders such as type I diabetes mellitus, thyroid diseases, vitiligo, and collagen tissue diseases. Dermatitis herpetiformis is, usually, successfully treated with dapsone and gluten-free diet

FEATURE EXTRACTION AND RECOGNITION ON TRAFFIC SIGN IMAGES

FEATURE EXTRACTION AND RECOGNITION ON TRAFFIC SIGN IMAGESAbstractIt is vital that the traffic signs used to ensure the order of the traffic are perceived by the drivers. Traffic signs have international standards that allow the driver to learn about the road and the environment while driving. Traffic sign recognition systems have recently started to be used in vehicles in order to improve traffic safety. Machine learning methods are used in the field of image recognition. Deep learning methods increase the classification success by extracting the hidden and interesting features in the image. Images contain many features and this situation can affect success in classification problems. It can also reveal the need for high-capacity hardware. In order to solve these problems, convolutional neural networks can be used to extract meaningful features from the image. In this study, we created a dataset containing 1500 images of 14 different traffic signs that are frequently used on Turkey highways. The features of the images in this dataset were extracted using convolutional neural networks from deep learning architectures. The 1000 features obtained were classified using the Random Forest method from machine learning algorithms. 93.7% success was achieved as a result of this classification process.Keywords: Classification, Convolution neural network, Feature extraction, Random forest, Traffic signsTRAFİK İŞARETİ GÖRÜNTÜLERİNDE ÖZELLİK ÇIKARMA VE TANIMAÖzetTrafiğin düzenini sağlamak amacıyla kullanılan trafik levhalarını sürücülerin algılaması hayati önem taşımaktadır. Sürüş esnasında sürücünün yol ve çevre hakkında bilgi edinebilmesini sağlayan trafik levhaları uluslararası standartlara sahiptir. Trafik levhası tanıma sistemleri son zamanlarda trafik güvenliğini arttırmak amacıyla araçlarda kullanılmaya başlamıştır. Makine öğrenmesi yöntemleri görüntü tanıma alanında kullanılmaktadır.  Derin öğrenme yöntemleri, görüntüde yer alan gizli ve ilginç özellikleri çıkarak sınıflandırma başarısını arttırmaktadır. Görüntüler çok sayıda özellik içermektedir ve bu durum sınıflandırma problemlerinde başarıyı etkileyebilmektedir. Ayrıca yüksek kapasiteli donanım gereksinimini de ortaya çıkarabilmektedir. Bu sorunların çözülebilmesi için görüntüden anlamlı özelliklerin çıkarılmasında konvolüsyonel sinir ağları kullanılabilmektedir. Bu çalışmada Türkiye’deki karayollarında sıklıkla kullanılan 14 farklı trafik levhasına ait 1500 görüntü içeren bir veriseti tarafımızca oluşturulmuştur. Bu veriseti kullanılarak derin öğrenme mimarilerinden konvolüsyonel sinir ağları kullanılarak görüntülerin özellikleri çıkarılmıştır. Elde edilen 1000 özellik makine öğrenmesi algoritmalarından Random Forest yöntemi kullanılarak sınıflandırılmıştır. Bu sınıflandırma işlemi sonucunda %93.7 başarı elde edilmiştir.Anahtar Kelimeler: Konvolüsyonel sinir ağları, Özellik çıkarma, Random forest, Sınıflandırma, Trafik işaretleri

Reservoir management through characterization of smart fields using capacitance-resistance models

Use of smart well technologies to improve the recovery has caught significant attention in the oil industry in the last decade. Capacitance-Resistance (CRM) methodology is a robust data-driven technique for reservoir surveillance. Reservoir sweep is a crucial part of efficient recovery, especially where significant investment is done by means of installation of smart wells that feature inflow control valves (ICVs) that are remotely controllable. However, as it is a relatively newer concept, effective use of this new technology has been a challenge. In this study, the objective is to present the efficient use of ICVs in intelligent fields through the integrated use of capacitance-resistance modeling and smart wells with ICVs. A standard realistic SPE reservoir simulation model of a waterflooding process is used in this study where the smart well ICVs are controlled with conditional statements called procedures in a fully commercial full-physics numerical reservoir simulator. The simulation data is utilized to build the CRM model to obtain the inter-well connectivities at the zonal level beyond only the inter-well connectivity data as smart wells provide control and information on the amount of injection into each layer or zone. Thus, after analyzing the CRM model to detect the inter-well connectivities at the zone/layer-level in an iterative way, the optimum injection not only at the well level but also at the perf/zone level is found. The workflow is outlined as well as the improvements in the results. The smart well technology has been challenged with the associated cost component thus, it is important to present the benefits of this technology with applications in more diverse cases with different workflows. It has been observed that a robust reservoir characterization in an intelligent field can provide an insight into the physics of reservoir including smart wells with ICVs. The results are presented in a comparative way against the base case to illustrate the incremental value of the use of ICVs along with key performance indicators. Most importantly, it has been shown that smart well use without a robust reservoir management strategy does not always lead to successful results. In reservoir management, it is not only important to catch the well level details but also see the big picture at the field level to improve the performance of the reservoirs beyond individual well performances taking into account the interference between wells. This method takes the reservoir surveillance to the next level where reservoir characterization is improved using smart field technologies and capacitance-resistance modeling as a robust cost-effective data-driven method

Failure behavior of composite laminates under four-point bending

In this study, failure behavior of fiber-reinforced composites under four-point bending is investigated. First, the tests are modeled analytically using the classical lamination theory (CLT). The maximum allowable moment resultants of [ 12]Toffaxis laminate as well as balanced and symmetric angle-ply [ 3/ 3]s composite laminates as a function of fiber orientation angle, , are obtained using Tsai-Wu, maximum stress, maximum strain, Hashin, Tsai-Hill, Hoffman, quadric surfaces, modified quadric surfaces, and Norris failure criteria. Second, the same tests are simulated using the finite element method (FEM). Thermal residual stresses are calculated and accounted for in the failure analysis. An analysis is conducted for optimal positioning of the supports so as to ensure that intralaminar failure modes dominate interlaminar (delamination) failure mode. A test setup is then constructed accordingly and experiments are conducted. The correlation of the predicted failure loads and the experimental results is discussed. The quadric surfaces criterion is found to correlate better with the experimental results among the chosen failure criteria for the selected configurations

Ozone Therapy and Hyperbaric Oxygen Treatment in Lung Injury in Septic Rats

Various therapeutic protocols were used for the management of sepsis including hyperbaric oxygen (HBO) therapy. It has been shown that ozone therapy (OT) reduced inflammation in several entities and exhibits some similarity with HBO in regard to mechanisms of action. We designed a study to evaluate the efficacy of OT in an experimental rat model of sepsis to compare with HBO. Male Wistar rats were divided into sham, sepsis+cefepime, sepsis+cefepime+HBO, and sepsis+cefepime+OT groups. Sepsis was induced by an intraperitoneal injection of Escherichia coli; HBO was administered twice daily; OT was set as intraperitoneal injections once a day. The treatments were continued for 5 days after the induction of sepsis. At the end of experiment, the lung tissues and blood samples were harvested for biochemical and histological analysis. Myeloperoxidase activities and oxidative stress parameters, and serum proinflammatory cytokine levels, IL-1β and TNF-α, were found to be ameliorated by the adjuvant use of HBO and OT in the lung tissue when compared with the antibiotherapy only group. Histologic evaluation of the lung tissue samples confirmed the biochemical outcome. Our data presented that both HBO and OT reduced inflammation and injury in the septic rats' lungs; a greater benefit was obtained for OT. The current study demonstrated that the administration of OT as well as HBO as adjuvant therapy may support antibiotherapy in protecting the lung against septic injury. HBO and OT reduced tissue oxidative stress, regulated the systemic inflammatory response, and abated cellular infiltration to the lung demonstrated by findings of MPO activity and histopathologic examination. These findings indicated that OT tended to be more effective than HBO, in particular regarding serum IL-1β, lung GSH-Px and histologic outcome

Establishment of a core outcome set for burn care research: development and international consensus

Objective: To develop a core outcome set for international burn research.Design: Development and international consensus, from April 2017 to November 2019.Methods: Candidate outcomes were identified from systematic reviews and stakeholder interviews. Through a Delphi survey, international clinicians, researchers, and UK patients prioritised outcomes. Anonymised feedback aimed to achieve consensus. Pre-defined criteria for retaining outcomes were agreed. A consensus meeting with voting was held to finalise the core outcome set.Results: Data source examination identified 1021 unique outcomes grouped into 88 candidate outcomes. Stakeholders in round 1 of the survey, included 668 health professionals from 77 countries (18% from low or low middle income countries) and 126 UK patients or carers. After round 1, one outcome was discarded, and 13 new outcomes added. After round 2, 69 items were discarded, leaving 31 outcomes for the consensus meeting. Outcome merging and voting, in two rounds, with prespecified thresholds agreed seven core outcomes: death, specified complications, ability to do daily tasks, wound healing, neuropathic pain and itch, psychological wellbeing, and return to school or work.Conclusions: This core outcome set caters for global burn research, and future trials are recommended to include measures of these outcomes