Search

The combination of high or low temperatures and high salt may cause significant harm to the yield, quality, and overall productivity of forage pea crops. The germination process, a crucial phase in the life cycle of forage peas, may be greatly influenced by varying temperature and salinity conditions. To comprehend the influence of these elements on the germination of forage peas, one must use many tactics, including the choice of resilient forage pea cultivars. The experiment aimed to evaluate the response of four forage pea cultivars (Arda, Ozkaynak, Taskent, and Tore) caused by various temperature (10 °C, 15 °C, and 20 °C) and salt (0, 5, 10, 15, and 20 dS m−1) conditions at the germination stage using multivariate analysis and machine learning methods. An observation of statistical significance (p < 0.01) was made regarding the variations between genotypes, temperature–salt levels, and the interaction of the observed factors: germination percentage (GP), shoot length (SL), root length (RL), fresh weight (FW), and dry weight (DW). The cultivar Tore had the best values for SL (1.63 cm), RL (5.38 cm), FW (1.10 g), and DW (0.13 g) among all the cultivars. On the other hand, the Ozkaynak cultivar had the highest value for GP (89.13%). The values of all of the parameters that were investigated decreased as the salt level rose, whereas the values increased when the temperature level increased. As a result, the Tore cultivar exhibited the highest values for shoot length, root length, fresh weight, and dry weight variables when exposed to a maximum temperature of 20 °C and a saline level of 0 dS m−1. It was determined that temperature treatment of fodder peas can reduce salt stress if kept at optimum levels. The effects of temperature and salt treatments on the germination data of several fodder pea cultivars were analyzed and predicted. Three distinct machine learning algorithms were used to create predictions. Based on R2 (0.899), MSE (5.344), MAPE (6.953), and MAD (4.125) measures, the MARS model predicted germination power (GP) better. The GPC model performed better in predicting shoot length (R2 = 0.922, MSE = 0.602, MAPE = 11.850, and MAD = 0.326) and root length (R2 = 0.900, MSE = 0.719, MAPE = 12.673, and MAD = 0.554), whereas the Xgboost model performed better in estimating fresh weight (R2 = 0.966, MSE = 0.130, MAPE = 11.635, and MAD = 0.090) and dry weight (R2 = 0.895, MSE = 0.021, MAPE = 12.395, and MAD = 0.013). The results of the research show that the techniques and analyses used can estimate stress tolerance, susceptibility levels, and other plant parameters, making it a cost-effective and reliable way to quickly and accurately study forage peas and related species.

The genus vetch (Vicia L.) is grown worldwide for fodder, hay, grain and silage, and rich in protein, mineral substances, vitamins and an essential source of roughage in animal husbandry. However, genetic characterization studies in vetch are minimal. In this study, the genetic characterization of a total 37 accessions of five vetches (Vicia L.) species was investigated using SSR markers. A total of 18 SSR markers were used, and eight of them were showed polymorphism and used for genetic analysis of vetch accessions. The total number of alleles was 35, and the average number of alleles for each locus was determined as 4.38. The average heterozygote rate was found to be 0.49. The polymorphism information content (PIC) value varied between 0.23 and 0.77, and the average value was 0.44. Although almost a clear distinction was observed among the species, very high similarities were found between some cultivars within the same species. This similarity may be due to the narrow structure of the vetch genome or the inability of the SSR markers used in this study to distinguish the narrow structure of the vetch genome. The results reported here will be contributed to future germplasm management efforts and for comparative studies in vetch.

Computer-aided automation systems for the detection of plant diseases represent a challenging and highly impactful research domain in the field of agriculture. Tomatoes, a major and globally significant agricultural commodity, are cultivated in large quantities. This study introduces a novel approach for the automated detection of diseases on tomato leaves, leveraging both classical machine learning methods and deep neural networks for image classification. Specifically, classical learning methods employed the local binary pattern (LBP) technique for feature extraction, while classification tasks were carried out using extreme learning machines, k-nearest neighborhood (kNN), and support vector machines (SVM). In contrast, a novel convolutional neural network (CNN) framework, complete with unique parameters and layers, was utilized for deep learning. The results of this study demonstrate that the proposed approach outperforms state-of-the-art studies in terms of accuracy. The classification process covered various scenarios, including binary classification (healthy vs. unhealthy), 6-class classification, and 10-class classification for distinguishing different types of diseases. The findings indicate that the CNN model consistently outperformed classical learning methods, achieving accuracy rates of 99.5%, 98.50%, and 97.0% for 2-class, 6-class, and 10-class classifications, respectively. Future research may explore the use of computer-aided automated systems to detect diseases in diverse plant species.

Although classic methods (such as ear tagging, marking, etc.) are generally used for animal identification and recognition, biometric methods have gained popularity in recent years due to the advantages they offer. Systems utilizing biometric markers have been developed for various purposes in animal management, including more effective and accurate tracking of animals, vaccination, disease management, and prevention of theft and fraud. Animals" irises, retinas, faces, muzzle, and body patterns contain unique biometric markers. The use of these markers in computer vision approaches for animal identification and tracking systems has become a highly effective and promising research area in recent years. This review aims to provide a general overview of the latest developments in image processing approaches for animal identification and recognition applications. In this review, we examined in detail all relevant studies we could access from different electronic databases for each biometric method. Afterward, the opportunities and challenges of classical and biometric methods were compared. We anticipate that this study, which conducts a literature review on animal identification and recognition based on computer vision approaches, will shed light on future research towards developing automated systems with biometric methods.

The COVID-19 pandemic, caused by a novel coronavirus, has become a global epidemic. Although the reverse transcription-polymerase chain reaction (RT-PCR) test is the current gold standard for detecting the virus, its low reliability has led to the use of CT and X-ray imaging in diagnostics. As limited vaccine availability necessitates rapid and accurate detection, this study applies k-means and fuzzy c-means segmentation to CT and X-ray images to classify COVID-19 cases as either diseased or healthy for CT scans and diseased, healthy, or non-COVID pneumonia for X-rays. Our research employs four open-access, widely-used datasets and is conducted in four stages: preprocessing, segmentation, feature extraction, and classification. During feature extraction, we employ the Gray-Level Co-Occurrence Matrix (GLCM), Local Binary Pattern (LBP), and Histogram of Oriented Gradients (HOG). In the classification process, our approach involves utilizing k-Nearest Neighbor (kNN), Support Vector Machines (SVM), and Extreme Learning Machines (ELM) techniques. Our research achieved a sensitivity rate exceeding 99%, which is higher than the 60-70% sensitivity rate of PCR tests. As a result, our study can serve as a decision support system that can help medical professionals make rapid and precise diagnoses with a high level of sensitivity.

Abstract Nuclear radiation detectors are indispensable for research in the field of nuclear radiation, X-ray spectroscopy and other areas. Interest in silicon p–i–n detectors of nuclear radiation is increasing today due to the possibility of their operation under normal conditions. In this paper, an equivalent circuit of a silicon–lithium p–i–n nuclear radiation detector is proposed. The proposed circuit is obtained using the classical Shockley equation for silicon semiconductors and the telegraph equations. The parameters of the equivalent circuit were determined using the multiple regression method. As a result of simulation of the model in the MATLAB Simulink graphical development environment, the amplitude-frequency and phase-frequency characteristics of the proposed model were obtained. Using the Monte Carlo method, the alpha-decay of the uranium isotope $${}_{92}{}^{233}\mathrm{U}$$ 92 233 U , thorium isotope $${}_{90}{}^{227}\mathrm{Th}$$ 90 227 Th and americium isotope $${}_{95}{}^{241}\mathrm{Am}$$ 95 241 Am the alpha-decay spectrum was obtained. Obtained alpha-decay spectra coincides with the experimental data, presented in previous works of other authors.

One of the methods of increasing output power of solar cells is increasing the concentration ratio using lenses and mirrors. Due to the temperature increase of silicon solar cells with concentrating lenses the system must be provided with an active cooling system. PV systems cooled with active or passive cooling methods using air, water or other substances are not taking into account the relationship between the power of solar radiation and operating power of the pump used in the cooling system, which is sufficiently complicated and little studied. This paper proposes a new active energy-efficient cooling system for silicon LCPV (Low Concentrating Photovoltaic). The idea of the proposed cooling system is to make the pump operate as efficiently as possible relative to the power of solar radiation and initial temperature of the heated solar cell. Performed experiments show that at fixed temperature and fixed solar radiation there is the most optimal pump operating power. Neural network is used to find this optimal pump power. Using neural networks requires large computing resources and development software which is not often available on local control computing devices nearby solar power plants. IoT (Internet of Things) technologies allow not only remote monitoring and control, but also forecasting the consumption of the cooling system depending on the current temperature and solar radiation power. Thus, energy efficiency of the cooling system is improved using neural networks and IoT technologies. The simulation of the cooling system operation was performed using various algorithms of the cooling system operation. The proposed active cooling system for silicon LCPV using IoT technologies allows the power consumption to be reduced by 60% compared to algorithms based on the threshold value of temperature.

Bu araştırmanın amacı, Türkiye’deki okullarda bürokratik yapı ile ilgili yapılan araştırmaların genel bir görünümü ile bu araştırma bulgularının tematik bir analizini sunmaktır. Sistematik inceleme metodu kullanılarak yürütülen araştırma, 2004-2020 yılları arasında Türkiye’de yapılan akademik çalışmaların topografik ve tematik bir analizini kapsamaktadır. Belirlenen dahil etme ve hariç tutma kriterlerine göre, Google Akademik ve Yüksek Öğretim Kurulu Ulusal Tez Merkezi’nin taranması sonucu araştırmaya onyedi lisans üstü tez, onbir araştırma makalesi olmak üzere toplam yirmi sekiz araştırma dahil edilmiştir. Araştırma mevcut çalışmaların büyük çoğunluğunun lisansüstü tez olduğunu ve nicel araştırma metotlarının araştırmacıların büyük çoğunluğu tarafından tercih edildiğini göstermektedir. Seçilen bu araştırmaların tematik analizi sonucunda, a) bürokratik yapı ve okul liderliği, b) bürokratik yapı, görüşler, tutum ve örgütsel davranış, c) bürokratik yapı ve mesleki öğrenme olmak üzere üç ana tema oluşturulmuş ve araştırma bulguları bu ana temalara göre tartışılmıştır. Araştırma, Türkiye’de okullardaki bürokratik yapının engelleyici bir unsur olarak görülmesinin yanında kolaylaştırıcı ve bireyleri olumlu etkileyici yönlerinin olduğunu da ortaya koymaktadır.

The main objective of this research is to present a novel design of three degrees of freedom (DOF) non-integrated runner permanent magnet spherical motor (NR-PMSM). In this proposed spherical motor, tilting and spinning movements are separated from each other instead of integrated motion, thus providing a more ergonomic design, easier control and increasing working performance. The NR-PMSM body block consists of stator, rotor and two covers and differs from known spherical motors with its modular design and different rotor bearings. Iron core stators are mounted separately on ABS blocks to create a lighter mid-motor body. A multi-point ball bearing mechanism is provided between the lower and upper covers, and in this way, more stable movement of the rotor is ensured without affecting the electromagnetic structure of the motor. Matlab and Ansys/Maxwell finite element programs are utilized in the electromagnetic modeling and numerical calculations of the spherical motor. At the end of all these works, the NR-PMSM prototype is realized and the angle, torque, power and speed tests of the motor are carried out by experimentally and they are compared with the simulation results. As a result of the experimental study, NR-PMSM consumes 197 W of power at a maximum speed of 156 r/min. It produces 0.04 kgf.cm spinning torque per watt consumed, tilting torque of 0.031 kgf.cm. The motor weight is 0.730 kg and it produces 10.5 kgf.cm spinning, 8.7 kgf.cm tilting torque per kg. The rotor can rotate around 360 0 full lap while moving with a maximum tilting angle of ±45°. It is possible to summarize the subject of this study, novel design NR-PMSM is completely different and original from the spherical motor researches in the literature and it can be preferred in robotic/manipulator/simulator applications as a innovative actuator system.

Automated code generation and process flow management are central to web-based application development today. This database-centric approach targets the form and process management challenges faced by corporate companies. It minimizes the time losses caused by managing hundreds of forms and processes, especially in large companies. Shortening development times, optimizing user interaction, and simplifying the code are critical advantages offered by this methodology. These low-code systems accelerate development, allowing organizations to adapt to the market quickly. This approach simplifies the development process with drag-and-drop features and enables developers to produce more effective solutions with less code. Automatic code generation with flow diagrams allows one to manage inter-page interactions and processes more intuitively. The interactive Process Design Editor developed in this study makes code generation more user-friendly and accessible. The case study results show that a 98.68% improvement in development processes, a 95.84% improvement in test conditions, and a 36.01% improvement in code size were achieved with this system. In conclusion, automated code generation and process flow management represent a significant evolution in web application development processes. This methodology both shortens development times and improves code quality. In the future, the demand for these technologies is expected to increase even more.

The work presented in this paper proposes a novel approach to tracking a specific vehicle over the video streams published by the collaborating traffic surveillance cameras. In recent years, smart, effective transportation systems and intelligent traffic management applications are among the topics that have been given importance by various institutions. Developing a scalable, fault-tolerant, and resilient traffic monitoring system that retrieves video chunks with the desired query is challenging. For these challenging problems, stream processing and data retrieval systems have been developed over the years. However, there are still existing shortcomings between users and retrieval systems. This paper investigates the problem of retrieving video chunks by key-value query based on publish/subscribe model. Thus, we propose a hybrid of an asynchronous and synchronous communication mechanism for the Event-Based Microservice framework. We aim to develop generic techniques for better utilization of existing platforms. In the proposed framework, (i) first of all, microservices detect vehicles and extract their type, color, and speed features, and stored them in the metadata repository. (ii) Microservices publish each feature as events (iii) Other microservices self-join subscribe to those events, which leads to more events being published by combing all the possibilities: type-color, type-speed, color-speed, and type-color-speed. Finally, (iv) the system visualizes the query result and system status in real-time. When the user has selected color or/and a type or/and a speed feature, the system will return the best-matched vehicles without re-processing the videos. Experimental results show that our proposed system filters messages in real-time and supports easy integration of new microservices with the existing system.

Today’s research on concentrated photovoltaic (CPV) cells focuses on creating multi-junction semiconductor solar cells capable of withstanding high temperatures without losing their properties. This paper investigated silicon low concentrated photovoltaic (LCPV) devices using Fresnel lenses. The parameters of the silicon CPV cell were measured to simulate its operation based on a single-diode model with four and five parameters. The most optimal position of the Fresnel lens relative to the solar cell was shown, and the dependence of the CPV efficiency on the concentration ratio, incident solar power, and temperature was studied. Experiments on heating of a solar cell were conducted to build a model of heating of a solar cell under different solar radiation based on machine learning. Additionally, a cooling system was developed, and experiments were conducted for one LCPV cell. The resulting LCPV model was used to predict electrical power output and temperature change pattern using clear day data. Results of modeling show increase in generated energy by 27% compared with non-concentrated solar cells. Cooling system energy consumption was simulated, and the optimum cooling regime was determined. The proposed LCPV system can be used as a hybrid heat and electricity source, increase power generation, and does not require new solar cell production technologies.

The polygon covering problem is an important class of problems in the area of computational geometry. There are slightly different versions of this problem depending on the types of polygons to be addressed. In this paper, we focus on finding an answer to a question of whether an orthogonal rectangle, or spatial query window, is fully covered by a set of orthogonal rectangles which are in smaller sizes. This problem is encountered in many application domains including object recognition/extraction/trace, spatial analyses, topological analyses, and augmented reality applications. In many real-world applications, in the cases of using traditional central computation techniques, working with real world data results in a performance bottlenecks. The work presented in this paper proposes a high performance MapReduce-based big data framework to solve the polygon covering problem in the cases of using a spatial query window and data are represented as a set of orthogonal rectangles. Orthogonal rectangular polygons are represented in the form of minimum bounding boxes. The spatial query windows are also called as range queries. The proposed spatial big data framework is evaluated in terms of horizontal scalability. In addition, efficiency and speed-up performance metrics for the proposed two algorithms are measured.

Örüntü tanıma psikolojiden biyometriye, biyoenformatikten gen ifadelerinin analizine, trafikten hesaplamalı finansa kadar birçok alanda kullanılmaktadır. Optik Karakter Tanıma da bu alanlardan bir tanesidir. Kamu ve özel birçok firma, arşivlerindeki klasörlenmiş verilerini taratarak dijital hale getirmekte ve bunun için emek yoğun çalışmalar yapmaktadır. Ancak resim olarak dijitalleştirilen bu verilerin içerik olarak aranması ve işlenmesi ancak operatörlerin manuel olarak taranan resim verisine meta veri eklemesi ile kısmi olarak gerçekleşmektedir. Bu çalışmada, resim olarak taranarak (eng. scan) ve dijital hale getirilen büyük miktarlardaki bu dokümanlar üzerinde içerik bazlı figür aramaları mümkün kılan bir mimari geliştirdik. Kullanıcı, bazı anahtar kelimelerle arama yaparak dijital dökümanlardaki ilgili figürleri başlıklarıyla beraber görüntüleyebilmektedir. Sistemin yapılabilirlik ve başarımı farklı veri setleri üzerinde test edilmiş, başarılı sonuçlar elde edilmiştir.

In this paper, we described a method of double-sided diffusion and drift of lithium-ions into monocrystalline silicon for the formation of the large-sized, p-i-n structured Si(Li) radiation detectors. The p-i-n structure is a p-n junction with a doped region, where the “i-region” is between the n and the p layers. A well-defined i-region is usually associated with p or n layers with high resistivities. The p-i-n structure is mostly used in diodes and in some types of semiconductor radiation detectors. The uniqueness of this method is that, in this method, the processes of diffusion and drift of lithium-ions, which are the main processes in the formation of Si(Li) p-i-n structures, are produced from both flat sides of cylindrical-shaped monocrystalline silicon, at optimal temperature (T = 420 °C) conditions of diffusion, and subsequently, with synchronous supply of temperature (from 55 to 100 °C) and reverse bias voltage (from 70 to 300 V) during drift of lithium-ions into silicon. Thus, shortening the manufacturing time of the detector and providing a more uniform distribution of lithium-ions in the crystal volume. Since, at present, the development of manufacturing of large-sized Si(Li) detectors is hindered due to difficulties in obtaining a uniformly compensated large area and time-consuming manufacturing process, the proposed method may open up new possibilities in detector manufacturing.

Improving the efficiency of solar panels is the main task of solar energy generation. One of the methods is a solar tracking system. One of the most important parameters of tracking systems is a precise orientation to the Sun. In this paper, the performance of single-axis solar trackers based on schedule and light dependent resistor (LDR) photosensors, as well as a stationary photovoltaic installation in various weather conditions, were compared. A comparative analysis of the operation of a manufactured schedule solar tracker and an LDR solar tracker in different weather conditions was performed; in addition, a simple method for determining the rotation angle of a solar tracker based on the encoder was proposed. Finally, the performance of the manufactured solar trackers was calculated, taking into account various weather conditions for one year. The proposed single-axis solar tracker based on schedule showed better results in cloudy and rainy weather conditions. The obtained results can be used for designing solar trackers in areas with a variable climate.

Traffic surveillance cameras are widely used in traffic management and information systems. Processing streaming media in real time is resource and time-consuming processes and even impossible to realize in most real-world applications. To overcome the performance problems in such applications, this article introduces a middleware system based on pub/sub messaging protocol and a dispatcher to preprocess the streams in real time. Experimental results show that proposed middleware may be utilized in different areas such as infrastructure planning, traffic management, and prevention of traffic offenses.

Doğal Dil İşleme; matematik, bilgisayar bilimi, yapay zekâ ve dilbilimi alanlarındaki çalışmaları kapsamaktadır. Yapay zekâ alanındaki çalışmaların hedeflerinden biri doğal dilde söyleneni anlamaktır. Doğal dili anlamaya yönelik birçok çalışma mevcuttur. Bu çalışmalardan bazıları matematik, fizik ve mekanik alandaki problemlerin anlaşılması ve çözülmesi ile ilgilidir. Bu çalışmada, ilkokul seviyesindeki matematik problemleri anlayabilen ve çözebilen bir aritmetik problem çözücü sistem önerilmektedir. Uygulama, kelimelerin morfolojik analizi, problem tipinin tanımlanması (toplama, çıkarma, çarpma, bölme) ve sonucun hesaplanması adımlarını içermektedir. Problem tipleri bir XML dokümanı yardımıyla belirlenmekte, bu doküman ise problem tiplerine karşılık gelen anahtar kelimeleri içermektedir. Bu uygulamanın temel katkısı, öğretmenlerin öğrencilerini daha iyi değerlendirmesi açısından kullanacakları eğitim materyallerini hazırlamalarına yardımcı olmaktır.

Doğal Dil İşleme; matematik, bilgisayar bilimi, yapay zekâ ve dilbilimi alanlarındaki çalışmaları kapsamaktadır. Yapay zekâ alanındaki çalışmaların hedeflerinden biri doğal dilde söyleneni anlamaktır. Doğal dili anlamaya yönelik birçok çalışma mevcuttur. Bu çalışmalardan bazıları matematik, fizik ve mekanik alandaki problemlerin anlaşılması ve çözülmesi ile ilgilidir. Bu çalışmada, ilkokul seviyesindeki matematik problemleri anlayabilen ve çözebilen bir aritmetik problem çözücü sistem önerilmektedir. Uygulama, kelimelerin morfolojik analizi, problem tipinin tanımlanması (toplama, çıkarma, çarpma, bölme) ve sonucun hesaplanması adımlarını içermektedir. Problem tipleri bir XML dokümanı yardımıyla belirlenmekte, bu doküman ise problem tiplerine karşılık gelen anahtar kelimeleri içermektedir. Bu uygulamanın temel katkısı, öğretmenlerin öğrencilerini daha iyi değerlendirmesi açısından kullanacakları eğitim materyallerini hazırlamalarına yardımcı olmaktır

Distributed image processing applications are constrained by the limited network bandwidth and huge image sizes. On the other hand, in some cases, applications containing images include spatial and topological queries not requiring pixel-based information. In such applications, using vector representations of raster images is expected to give better performance results. In this paper, we setup a scenario to transfer a Landsat Gokceada satellite image from one machine to another through web services. We perform the tests for both vector and raster forms of the same image and analyze transmission and response times.

Abstract Background Oxidative stress could play a role in pathogenesis of hepatitis C virus (HCV) infection. The aim of our study is to determine oxidant/antioxidant status of patients with chronic hepatitis C (CHC), and the effect of pegylated interferon alfa-2b plus ribavirin combination therapy on oxidative stress. Methods Nineteen patients with chronic HCV infection and 28 healthy controls were included in the study. In control and patient groups, serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels, erythrocyte malondialdehyde (MDA) levels, erythrocyte CuZn-superoxide dismutase (SOD), erythrocyte glutathione peroxidase (GSH-Px) activities were measured. After pegylated interferon alfa-2b and ribavirin combination therapy for 48 weeks, these parameters were measured again in the patient group. Results Serum MDA levels increased significantly in CHC patients (n:19), before the treatment when compared with healthy subjects (n:28) 9.28 ± 1.61, 4.20 ± 1.47 nmol/ml, p < 0.001 respectively. MDA concentration decreased significantly (p < 0.001) after the treatment as well as ALT, AST activity, in erythrocytes of these patients. Average antioxidant enzymes (superoxide dismutase and glutathione peroxidase) were significantly lower in erythrocytes of patients with CHC before treatment compared with the control group (both, p < 0.001). Chronic Hepatitis C patients after pegylated interferon alfa-2b and ribavirin therapy showed values of SOD, GSH-Px were significantly higher than pretreatment levels (both, p < 0.001). Conclusion Our results show that patients with chronic HCV infection are under the influence of oxidative stress associated with lower levels of antioxidant enzymes. These impairments return to level of healthy controls after pegylated interferon alfa-2b plus ribavirin combination therapy of CHC patients. Although interferon and ribavirin are not antioxidants, their antiviral capacity might reduce viral load, and inflammation, and perhaps through this mechanism might reduce virus-induced oxidative stress.