A low cost shading analyzer and site evaluator design to determine solar power system installation area

Shading analyzer systems are necessary for selecting the most suitable installation site to sustain enough solar power. Afterwards, changes in solar data throughout the year must be evaluated along with the identification of obstructions surrounding the installation site in order to analyze shading effects on productivity of the solar power system. In this study, the shading analysis tools are introduced briefly, and a new and different device is developed and explained to analyze shading effect of the environmental obstruction on the site on which the solar power system will be established. Thus, exposure duration of the PV panels to the sunlight can be measured effectively. The device is explained with an application on the installation area selected as a pilot site, Denizli, in Turkey. © 2015 Selami Kesler et al

RSS-based wireless LAN indoor localization and tracking using deep architectures

Wireless Local Area Network (WLAN) positioning is a challenging task indoors due to environmental constraints and the unpredictable behavior of signal propagation, even at a fixed location. The aim of this work is to develop deep learning-based approaches for indoor localization and tracking by utilizing Received Signal Strength (RSS). The study proposes Multi-Layer Perceptron (MLP), One and Two Dimensional Convolutional Neural Networks (1D CNN and 2D CNN), and Long Short Term Memory (LSTM) deep networks architectures for WLAN indoor positioning based on the data obtained by actual RSS measurements from an existing WLAN infrastructure in a mobile user scenario. The results, using different types of deep architectures including MLP, CNNs, and LSTMs with existing WLAN algorithms, are presented. The Root Mean Square Error (RMSE) is used as the assessment criterion. The proposed LSTM Model 2 achieved a dynamic positioning RMSE error of 1.73 m, which outperforms probabilistic WLAN algorithms such as Memoryless Positioning (RMSE: 10.35 m) and Nonparametric Information (NI) filter with variable acceleration (RMSE: 5.2 m) under the same experiment environment.ECSEL Joint Undertaking ; European Union's H2020 Framework Programme (H2020/2014-2020) Grant ; National Authority TUBITA

Gen verileri üzerinde ilginçlik ölçütleri kullanılarak birliktelik kuralları madenciliğinin uygulanması

Aim: Data mining is the discovery process of beneficial information, not revealed from large-scale data beforehand. One of the fields in which data mining is widely used is health. With data mining, the diagnosis and treatment of the disease and the risk factors affecting the disease can be determined quickly. Association rules are one of the data mining techniques. The aim of this study is to determine patient profiles by obtaining strong association rules with the apriori algorithm, which is one of the association rule algorithms. Material and Method: The data set used in the study consists of 205 acute myocardial infarction (AMI) patients. The patients have also carried the genotype of the FNDC5 (rs3480, rs726344, rs16835198) polymorphisms. Support and confidence measures are used to evaluate the rules obtained in the Apriori algorithm. The rules obtained by these measures are correct but not strong. Therefore, interest measures are used, besides two basic measures, with the aim of obtaining stronger rules. In this study For reaching stronger rules, interest measures lift, conviction, certainty factor, cosine, phi and mutual information are applied. Results: In this study, 108 rules were obtained. The proposed interest measures were implemented to reach stronger rules and as a result 29 of the rules were qualified as strong. Conclusion: As a result, stronger rules have been obtained with the use of interest measures in the clinical decision making process. Thanks to the strong rules obtained, it will facilitate the patient profile determination and clinical decision-making process of AMI patients.Amaç: Veri madenciliği, önceden büyük ölçekli verilerden ortaya çıkarılmayan faydalı bilgilerin keşfedilme sürecidir. Veri madenciliğinin yaygın olarak kullanıldığı alanlardan biri de sağlıktır. Veri madenciliği ile hastalığın tanı ve tedavisi ile hastalığı etkileyen risk faktörleri hızlı bir şekilde belirlenebilmektedir. Birliktelik kuralları, veri madenciliği tekniklerinden biridir. Bu çalışmanın amacı, birliktelik kuralı algoritmalarından biri olan apriori algoritması ile güçlü birliktelik kuralları elde ederek hasta profillerini belirlemektir. Materyal ve Metot: Çalışmada kullanılan veri seti 205 akut miyokard enfarktüsü (AMI) hastasından oluşmaktadır. Hastalar ayrıca FNDC5 polimorfizmlerinin rs3480, rs726344, rs16835198 genotipini de taşımaktadır. Apriori algoritması ile elde edilen kuralları değerlendirmek için destek ve güven ölçüleri kullanılır. Ancak bu ölçütler ile elde edilen kurallar doğrudur ancak güçlü değildir. Bu nedenle, daha güçlü kurallar elde etmek amacıyla iki temel ölçütün yanı sıra ilginçlik ölçütleri kullanılmaktadır. Bu çalışmada daha güçlü kurallara ulaşmak için ilginçlik ölçütlerinden kaldıraç, kanaat, kesinlik faktörü, cosine, korelasyon katsayısı (phi) ve karşılıklı bilgi ölçütleri uygulanmıştır. Bulgular: Çalışmada 108 kural elde edilmiştir. Bu kurallara ilginçlik ölçütlerinin de uygulanması ile elde edilen kural sayısı 29 olmuştur ve bu kurallar güçlü kural olarak nitelendirilmiştir. Sonuç: Sonuç olarak, klinik karar verme sürecinde ilginçlik ölçütlerinin kullanılmasıyla daha güçlü kurallar elde edilmiştir. Elde edilen güçlü kurallar sayesinde AMİ hastalarının hasta profili belirleme ve klinik karar verme sürecini kolaylaştıracaktır

The Types of Injury, Regions and Frequency in Athletes Participating Universities Taekwondo Championchip

This study aims to determine the rates of injuries, the types of injuries and what part of the body is mostly injured in professional taekwondo sportsmen during competitions. This study involves 287 sportsmen participated in the interuniversity Taekwando championship in Ordu. Injuries during a match of the championship was determined by a team. The characteristics and types of injuries, the sportsmen’s verbal expressions were registered by a pre-determined team, and it was mentioned about how these injuries occurred, in which part of the body the injuries were seen. Among 287 sportsmen in this study, 178 sportsmen were male, 109 ones were female. 46 universities took part in this championship. The championship lasted three days and total 271 competitions were done. 539 injuries were determined in total. The mostly seen type of injury was hematoma (43%), the mostly injured part was in lower extremities 416 (77%), most of the injuries were ones seen in defense (36%). No injuries were not observed in neck, shoulder, spine or trunk and cerebral injuries were not also observed. As a result of the study, it was explained that most of the injuries seen in the taekwondo competitions did not require the medical intervention and the lower extremities were mostly injured in these competitions. In the light of these findings, it could be said that these parts be protected during the competitions and the defense techniques be different

The association between serum serglycin level and coronary artery disease severity in patients with stable angina pectoris

Background: Serglycin plays a key role in the inflammatory status however the relationship between coronary artery disease (CAD) and serglycin is still unknown. Aim: In this study, we aimed to investigate association of serglycin levels with CAD severity in patients with stable angina pectoris (SAP). Methods: In total, 100 SAP patients diagnosed by coronary angiography and clinical manifestations, and 100 control subjects matched for age and sex were enrolled in this case-control study. Plasma levels of serglycin, high-sensitivity C-reactive protein (hsCRP), lipid profiles, and clinical parameters were assayed for all participants. The severity of coronary lesions was evaluated based on the SYNTAX score (SS) assessed by coronary angiography. Results: Positively correlated with the SS (r = 0.564, p < 0.001), the plasma serglycin level in the SAP group was higher than that in the control group (11.17 ± 1.82 vs. 19.28 ± 1.88 ng/mL, p < 0.001). The plasma serglycin level was an inde-pendent predictor for both SAP (odds ratio [OR] 1.037, 95% confidence interval [CI] 1.020–1.054, p < 0.001) and a high SS (OR = 1.087, 95% CI 1.051–1.124, p < 0.001) in a multivariate logistic regression model. In the receiver operating characteristic curve analysis, the plasma serglycin level was found to have a better predictive value for a high SS (area under the curve [AUC] 0.858, 95% CI 0.788–0.929, p < 0.001) compared with hsCRP (AUC 0.665, 95% CI 0.557–0.773, p = 0.006; Z = 2.94, p < 0.001), with an optimal cut-off value of 17.25 ng/mL (sensitivity 94.3%, specificity 68.2%). Conclusions: Plasma serglycin levels correlate with both the presence and severity of coronary stenosis in patients with SAP, suggesting that it could be a potential predictive marker of severe stenosis in SAP patients

Mikrokalsifikasyonların Tanısında Vakum Destekli Stereotaktik Meme Biyopsisi:Üç Yıllık Deneyimlerimiz

Amaç: Vakum destekli stereotaktik meme biyopsisi VDSB , günümüzde giderek artan sıklıkla kullanılan, gereksiz eksizyonel biyopsileri önleyebilecek, basit, güvenli, minimal invaziv bir perkütan biyopsi yöntemidir. Bu geriye dönük çalışmanın amacı, mikrokalsifikasyonların tanısında kullandığımız VDSB ile ilgili deneyimlerimizi sunmaktır. Gereç ve Yöntem: Selçuk Üniversitesi Tıp Fakültesi Hastanesi’nde, 2010-2013 yılları arasında mamografisinde mikrokalsifikasyon saptanmış ve VDSB uygulanmış 46 olgunun dosya bilgileri geriye dönük olarak incelenmiştir. Mikrokalsifikasyonların özellikleri, VDSB uygulamaları sırasında ve sonrasında karşılaşılan erken ve geç dönem komplikasyonlar, histopatolojik sonuçlar, takip sonuçları değerlendirilmiş ve sonuçlar hasta sayısı, yüzde ve ortalama ± standart sapma olarak sunulmuştur. Bulgular: VDSB yapılan 46 olguya ait mamogramlarda en çok küme oluşturan pleomorfik %32,6 mikrokalsifikasyonlar görülmüştür.VDSB’ye bağlı erken dönem komplikasyonlar %15,2 olguda ağrı, %2,2 olguda hematom, % 2,2 olguda ise ekimozdur. Olguların hiç birisinde geç dönemde komplikasyon ile karşılaşılmamıştır. 29 olguda % 63 histopatoloji benign, 17 olguda %37.0 ise malign olarak sonuçlanmıştır. 11 %23,9 olguda saptanan duktal karsinoma insitu, bir olguda %2,2 saptanan lobüler karsinoma insitu, dört %8,7 olguda saptanan atipik duktal hiperplazi göz önüne alındığında, toplam 16 olguda %34,7 tümör henüz prekürsor iken veya hücre içi aşamada yakalanmıştır. Sonuç: VDSB, özellikle mamografik mikrokalsifikasyonların tanısında cerrahi biyopsilere göre öncelikle tercih edilebilecek minimal invaziv bir yöntemdir. Benign olgularda hasta için anksiyete ve morbidite kaynağı olabilecek gereksiz cerrahi girişimleri önlemekte malign olgularda ise klinisyene tedavi planında yol gösterici olmaktadı

Application of Diatom Indices to Assess Water Quality of the Akarçay Stream (Afyonkarahisar, Turkey)

Akarçay’ın bentik diyatomeleri ve bazı fiziko-kimyasal özellikleri Mart-Aralık 2008 tarihleri arasında dört istasyondan aylık olarak alınan örneklerde incelenmiştir. Çayın başlangıç kısmında Cocconeis placentula, Cyclotella meneghiniana, Encyonema minutum, Sellaphora pupula, Nitzschia tubicola, Cymatopleura solea, Amphora veneta, Amphora pediculus, Ulnaria ulna, Gomphonema parvulum, Gomphonema angustatum ve Navicula cryptocephala bentik diyotome topluluğunda dominant diyatome türleri olmuşlardır. Çayın aşağı kısmında ise, Nitzschia palea bentik diyatome topluluğunda dominant olmuştur. Diyatome indeksleri ile TÇM, NH4-N, NO2-N, PO4-P, BOİ5 ve KOİ arasında kuvvetli pozitif ve çözünmüş oksijen ile kuvvetli negatif korelasyon göstermiştir. Diyatome indeksleri ve fiziko-kimyasal analiz sonuçları çayın başlangıç kısımlarının orta derecede kirlenmiş, çayın son kısımlarının ise aşırı derecede kirlenmiş olduğunu göstermiştir

A neural network-based approach for calculating dissolved oxygen profiles in reservoirs

A Neural Network (NN) modelling approach has been shown to be successful in calculating pseudo steady state time and space dependent Dissolved Oxygen (DO) concentrations in three separate reservoirs with different characteristics using limited number of input variables. The Levenberg-Marquardt algorithm was adopted during training. Pre-processing before training and post processing after simulation steps were the treatments applied to raw data and predictions respectively. Generalisation was improved and over-fitting problems were eliminated: Early stopping method was applied for improving generalisation. The correlation coefficients between neural network estimates and field measurements were as high as 0.98 for two of the reservoirs with experiments that involve double layer neural network structure with 30 neurons within each hidden layer. A simple one layer neural network structure with 11 neurons has yielded comparable and satisfactorily high correlation coefficients for complete data set, and training, validation and test sets of the third reservoir