Ana Bileşenler Analizi Yardımıyla Orta ve Yüksek Çözünürlükteki Uydu Görüntülerinin İncelenmesi

DergiPark: 245876trakyafbdThe objective of this study was to determine and compare the principal components for different satellite imagery in the same study area. Five different remote sensing data sources were tested. They are: (a) (i) the moderate resolution satellite images from the Landsat Enhanced Thematic Mapper Plus (ETM+), (ii) the Indian Remote Sensing Satellite (IRS), and (iii) French Satellite Pour l'Observation de la Terre (SPOT) and (b) (iv) high-resolution satellite images from IKONOS and (v) airborne hyperspectral images taken by the Compact Airborne Spectral Imaging system (CASI). Among all the principle components (PCs) for all the datasets, the first three PCs contain most of the variance of the original datasets and all the other PC bands contain noise for both moderate and high-resolution images. From these results, it was concluded that instead of original images the first three PCs could be used for classifications in agricultural and wetland areas.Çalışmanin amacı, aynı çalışma alanında farklı uydu görüntüleri için ana bileşenlerin (PCs) belirlenmesi ve karşılaştırılmasıdır. Bu amaçla beş değişik uydu görüntüsü test edildi. Bunlar: (a) orta çözünürlükteki (20-30m) uydu görüntüleri: (1) Amerikan Landsat Enhanced Thematic Mapper Plus (ETM+), (2) Hindistan Remote Sensing Satellite (IRS), ve (3)Fransız Satellite Pour l'Observation de la Terre (SPOT) ve (b) yüksek çözünürlükteki uydu görüntüleri (1) (4m) Amerikan IKONOS ve (2) Kanada teknoloji yüksek çözünürlükteki çok kanallı hava görüntüsüdür (1m) (CASI). Orta ve yüksek çözünürlükteki görüntülerin ana bileşenler analizi (PCA) sonuçları karşılaştırıldığında, ilk üç bileşenlerinin orijinal uydu görüntüsünün % 99.9’unu temsil ettiği tespit edilmiştir, geri kalan kanalların ise gürültü sinyallerinden oluştuğu görülmüştür. Bu veriler doğrultusunda, tarım ve ıslak alanlar için yapılacak bitki örtüsü sınıflandırmalarında ilk üç bileşenin, orijinal görüntülerin yerine kullanılmasının tercih edilebileceği belirlenmiştir

Landsat TM ve IRS Uydu Görüntülerinin Arazi Kullanımı ve Bitki Örtüsü Değişimlerini Belirleme Çalışmalarında Kullanımlarının Karşılaştırılması

DergiPark: 245880trakyafbdBu çalışmanın ana hedefi Landsat TM ve IRS görüntülerinin arazi sınıflaması sonuçlarının benzerlik gösterip göstermediği araştırmaktır. Kontrollü sınıflama yapmak için dijital hava fotoğrafları, çalışmada yer alan araştırmacıların önceki çalışmaları ve varolan arazi kulanım ve bitki örtüsü haritalarından faydalanılmıştır. Araştırma sonucunda, “ormanlık alanlarda”, “açık su alanlarında”, “meyve bahçelerinde” ve “palmetto ağaç” sınıflarında, her iki uydu görüntüsü (Landsat TM IRS) arasında benzerlikler olduğu gözlenmiştir. Buna karşın “açık alanlarda”, “sebze ve otlak alanlarda” ve “bataklıklarda” oldukça farklılıklar gözlenmiştir. Genel toplam doğruluk analiz sonuçlarına göre Landsat TM %86.3 ve IRS %88.4 doğrulukta kategorileri sınıflamıştır. Buna karşın golf alanı kategorilerindeki sınıflamada problem olduğu gözlenmiştir. Zamana bağlı çözünürlükte Landsat TM ve IRS görüntülerinin arasındaki 6 haftalık bir zaman diliminin bazı sorunların oluşmasına ve kategoriler arasında sınıflama hataların meydana gelmesine neden olduğu düşünülmüştür. Araştırmaya göre Landsat TM ve IRS görüntülerinin arazi kulanım ve bitki örtüsü değişim çalışmalarında birlikte kullanılmasının mümkün olduğu tespit edilmiştir.The objective of this research focuses on comparing Landsat TM and IRS data and determining if similar classification can be achieved from datasets for certain land cover types. Supervised classification was performed using information from a combination of digital aerial photographs, a priori knowledge of the study site by the authors and existing Land Use Land Cover (LULC) maps. The “upland forest,” “open water,” “tree crops” and “palmetto prairie” categories show strong agreement in terms of percentage of LULC found in both Landsat TM+ and IRS classified images. Conversely, the “open land,” “cropland and pastureland” and “wetlands” categories display differences based on the land cover area. Based on the overall classification accuracies similar results were produced for both TM and IRS data of 86.3% and 88.4% respectively. On the other hand, certain LULC categories did not perform so well, such as the golf course. Temporal resolution between the TM and IRS images was six weeks, and this was considered a factor in the confusion between LULC category discrimination. This study showed that using Landsat TM and IRS in same study provide promising results for LULC studies

Ultrasound guided percutaneous radiofrequency thermal ablation of symptomatic uterine fibroids — results from a single center and 52 weeks of follow up

Objectives: Uterine fibroids are one of the most common female disorder of the reproductive age and may cause abnormal uterine bleeding (UAB), pain or infertility. Our aim was to evaluate the safety and efficacy of percutaneous radio frequency ablation (RFA) in reducing clinical symptoms, fibroid volume and improving laboratory parameters.Material and methods: Thirty-five symptomatic patients with 54 uterine fibroids were enrolled. Preintervention evaluation was made for each participant and included ultrasonography to assess the volume, largest diameter and location of the fibroid and Visual Analogue Scale (VAS) for quantifying the degree of menstrual pain. The magnitude of menstrual bleeding was scored for each patient by using pictogram. Preprocedural laboratory assessment included hemoglobulin and hematocrit. Treatment efficacy was evaluated at 3, 6 and 12 months after the intervention with ultrasound (US) measurements,symptom scores and laboratory parameters.Results: Pretreatment mean Hb was significantly lower than those at 3, 6 and 12 month post treatment visits (p < 0.001). The pretreatment median volume was significantly higher than the median volumes measured at 3, 6 and 12 months after RFA (p < 0.001). Visual Analogue Score (VAS) for pain was significantly lower than baseline values at 6 and 12 month visits (p < 0.01). Pretreatment bleeding scores and the number of patients in the predefined severe bleeding category were significantly decreased.Conclusions: US guided RF ablation of uterine fibroids is relatively safe and effective procedure. It can be applied to the fibroids with varying localizations and sizes. It reduces the fibroid volume and obviate a need for more invasive treatment

Determination of water stress with spectral reflectance on sweet corn (Zea mays L.) using classification tree (CT) analysis

Abstract Water stress is one of the most important growth limiting factors in crop production. Several methods have been used to detect and evaluate the effect of water stress on plants. The use of remote sensing is deemed particularly and practically suitable for assessing water stress and implementing appropriate management strategies because it presents unique advantages of repeatability, accuracy, and cost-effectiveness over the ground-based surveys for water stress detection. The objectives of this study were to 1) determine the effect of water stress on sweet corn (Zea mays L.) using spectral indices and chlorophyll readings and 2) evaluate the reflectance spectra using the classification tree (CT) method for distinguishing water stress levels/severity. Spectral measurements and chlorophyll readings were taken on sweet corn exposed to four levels of water stress with 0, 33, 66 and 100 % of pot capacity (PC) before and after each watering time. The results demonstrated that reflectance in the red portion (600-700 nm) of the electromagnetic spectrum decreased and increased in the near infrared (NIR) region (700-900 nm) with the increasing field capacity of water level. Reflectance measured before the irrigation was generally higher than after irrigation in the NIR region and lower in the red region. However, when the four levels of PC and before or after irrigation only were compared, reflectance spectra indicated that water stressed corn plants absorbed less light in the visible and more light in the NIR regions of the spectrum than the less water stressed and unstressed plants. There was a similar trend to reflectance behaviour of water stress levels using chlorophyll readings that decreased over time. The CT analysis revealed that water stress can be assessed and differentiated using chlorophyll readings and reflectance data when transformed into spectral vegetation indices

Quantitative trait loci conferring grain mineral nutrient concentrations in durum wheat 3 wild emmer wheat RIL population

Mineral nutrient malnutrition, and particularly deficiency in zinc and iron, afflicts over 3 billion people worldwide. Wild emmer wheat, Triticum turgidum ssp. dicoccoides, genepool harbors a rich allelic repertoire for mineral nutrients in the grain. The genetic and physiological basis of grain protein, micronutrients (zinc, iron, copper and manganese) and macronutrients (calcium, magnesium, potassium, phosphorus and sulfur) concentration was studied in tetraploid wheat population of 152 recombinant inbred lines (RILs), derived from a cross between durum wheat (cv. Langdon) and wild emmer (accession G18-16). Wide genetic variation was found among the RILs for all grain minerals, with considerable transgressive effect. A total of 82 QTLs were mapped for 10 minerals with LOD score range of 3.2–16.7. Most QTLs were in favor of the wild allele (50 QTLs). Fourteen pairs of QTLs for the same trait were mapped to seemingly homoeologous positions, reflecting synteny between the A and B genomes. Significant positive correlation was found between grain protein concentration (GPC), Zn, Fe and Cu, which was supported by significant overlap between the respective QTLs, suggesting common physiological and/or genetic factors controlling the concentrations of these mineral nutrients. Few genomic regions (chromosomes 2A, 5A, 6B and 7A) were found to harbor clusters of QTLs for GPC and other nutrients. These identified QTLs may facilitate the use of wild alleles for improving grain nutritional quality of elite wheat cultivars, especially in terms of protein, Zn and Fe

FABRICATION AND SIMULATION OF FEEDSTOCK FOR TITANIUM-POWDER INJECTION-MOLDING CORTICAL-BONE SCREWS

WOS: 000490555500001Titanium-powder injection molding is a combination of plastic injection and powder metallurgy. Using this technology, near-net titanium parts are produced. In this study, feedstock-development experiments were performed with Ti-6Al-4V powders and binders for the production of titanium cortical-bone screws. Critical solid-loading and optimum solid-loading values were determined to specify the most appropriate binder system and ratio by volume. The critical solid-loading rate was determined as 62 % by volume while the optimum solid-loading rate was 60 % by volume. The rheological properties of the feedstocks were obtained with a capillary rheometer, and the thermal properties with a TGA analysis. The rheological behavior and thermal properties of PW/PE/SA and PEG8000/PP/SA feedstocks at different mixing ratios were determined. A simulation of the flow was made with the Moldflow program, designing a screw and a mold. For the two different feedstocks, skeletal binders PP and PE were identified and simulations were carried out. The knowledge that the feedstock skeletal-binder properties predominate was obtained as the data for the flow in the mold in the light. Experiments and simulations showed that the water-based feedstock is a suitable binder system for the cortical-bone screw molding

Price Prediction and Determination of the Affecting Variables of the Real Estate by Using X-Means Clustering and CART Decision Trees

The use of machine learning in real estate is quite new. When the working area is large, the factors affecting the price may vary according to the geographical regions and socioeconomic factors. It is thought that the price prediction performance of a model that will reflect these differences will be more successful than a general model. Unsupervised learning methods can be used both to increase performance and to show the variation of different factors affecting the price according to regions. With this aim, a hybrid model of X-Means clustering and CART decision trees was established in this study.  This model successfully learned the geographical and physical variables that affect the price. The prediction performance of the model was compared with the direct capitalization method, which is the gold standard in the domain. The hybrid model has a superior performance over direct capitalization in terms of mean square error, root mean square error and adjusted R-Squared metrics. The scores were 72.86, 0.0057 and 0.978, respectively. The effect of clustering was also examined. Clustering increased the prediction performance by 36%.

Price Prediction and Determination of the Affecting Variables of the Real Estate by Using X-Means Clustering and CART Decision Trees

The use of machine learning in real estate is quite new. When the working area is large, the factors affecting the price may vary according to the geographical regions and socioeconomic factors. It is thought that the price prediction performance of a model that will reflect these differences will be more successful than a general model. Unsupervised learning methods can be used both to increase performance and to show the variation of different factors affecting the price according to regions. With this aim, a hybrid model of X-Means clustering and CART decision trees was established in this study.  This model successfully learned the geographical and physical variables that affect the price. The prediction performance of the model was compared with the direct capitalization method, which is the gold standard in the domain. The hybrid model has a superior performance over direct capitalization in terms of mean square error, root mean square error and adjusted R-Squared metrics. The scores were 72.86, 0.0057 and 0.978, respectively. The effect of clustering was also examined. Clustering increased the prediction performance by 36%.

FABRICATION AND SIMULATION OF FEEDSTOCK FOR TITANIUM-POWDER INJECTION-MOLDING CORTICAL-BONE SCREWS

Titanium-powder injection molding is a combination of plastic injection and powder metallurgy. Using this technology, near-net titanium parts are produced. In this study, feedstock-development experiments were performed with Ti-6Al-4V powders and binders for the production of titanium cortical-bone screws. Critical solid-loading and optimum solid-loading values were determined to specify the most appropriate binder system and ratio by volume. The critical solid-loading rate was determined as 62 \% by volume while the optimum solid-loading rate was 60 \% by volume. The rheological properties of the feedstocks were obtained with a capillary rheometer, and the thermal properties with a TGA analysis. The rheological behavior and thermal properties of PW/PE/SA and PEG8000/PP/SA feedstocks at different mixing ratios were determined. A simulation of the flow was made with the Moldflow program, designing a screw and a mold. For the two different feedstocks, skeletal binders PP and PE were identified and simulations were carried out. The knowledge that the feedstock skeletal-binder properties predominate was obtained as the data for the flow in the mold in the light. Experiments and simulations showed that the water-based feedstock is a suitable binder system for the cortical-bone screw molding