European Journal of Science and Technology, Yıl 2024 Sayı 54

A Novel Blockchain-Based Application for Real Estate Management System

Havva UsluHavva Uslu ,Refik Samet ,Merve Ozkan,Okay — 2024-12-01

Traditionally, real estate management systems are based on a centralized approach where a single entity controls and manages all aspects of a real estate. However, centralized systems can compromise data security and integrity when issues arise. With the emergence of blockchain technology, it is now possible to create decentralized systems using smart contracts to automate various tasks and reduce the need for intermediaries. In this study, a real estate management system leveraging smart contracts and the decentralized structure of blockchain to facilitate the buying and selling of real estates has been developed. The developed application has successfully completed real estate transactions, and transaction information data has been recorded on the blockchain. The benefits of using a decentralized system, such as reducing transaction costs and increasing security, have been observed. This application not only reduces the physically conducted processes in a real estate transaction but also brings transparency to the real estate buying and selling process, contributing to the solving of trust issues between buyers and sellers in property transfers.

Effect of Rosemary Oleoresin on Some Quality Characteristics and Stability of Heat-Treated Sucuk

Meltem KaramahmutoğluMeltem Karamahmutoğlu ,Ayça Özden ,Güzin Kaban — 2024-12-01

The aim of the study was to determine the effect of rosemary oleoresin on some quality characteristics and stability of heat-treated sucuk. For this purpose, three different heat-treated sucuk batters were prepared: control (without antioxidant), ascorbic acid (AA) (500 mg/kg), rosemary oleoresin (RO) (250 mg/kg). After the fermentation, heat treatment and drying stages, the samples were packaged applying modified atmosphere (70% N2 + 30% CO2) and stored at 4 ?. Samples taken on days 0, 60, 90 and 120 of cold storage were subjected to pH, thiobarbutyric acid reactive substances (TBARS) and microbiological analyses. Additionally, at the end of storage, the samples were subjected to sensory analysis. No significant change was observed in the numbers of lactic acid bacteria and Micrococcus/Staphylococcus during storage. The use of AA and RO did not have any effect on these microorganisms. Enterobacteriaceae and yeast/mould numbers were detected below the detectable limit (<2 log cfu/g) during storage in all groups. The use of both RO and AA did not show a significant effect on the pH value of heat-treated sucuk. No significant change in pH value was observed also during storage (P>0,05). The level of TBARS value increased with increasing storage time (P <0,01). However, the difference between the mean TBARS values of the 90th and 120th days of storage was not found to be statistically significant (P>0,05). AA and RO groups gave lower mean TBARS values than the control group. However, no significant difference was observed in terms of mean TBARS value between AA and RO groups. In sensory analysis, RO and AA were evaluated with higher scores in terms of color, and RO was evaluated with higher scores than the control in terms of taste and general acceptability.

Design of A Circularly Polarized Microstrip Patch Antenna with Cross-Shaped Directors

İrem Karataşİrem Karataş ,Gülden Günay Bulut Öner ,Suad Başbuğ — 2024-12-01

In this paper, a circularly polarized microstrip patch antenna with two cross-shaped directors is proposed. The patch antenna is first designed in the form of a traditional linear polarized antenna. Then, two opposed corners of the patch are truncated to give a circular polarization property to the antenna. In the last design phase, two cross-shaped directors are added in front of the patch surface of the microstrip antenna to increase its gain. All adjustable geometrical properties of the antenna are optimized by parameter sweep method for each design phase. The antenna gain is increased from 3.99 dBi to 4.402 dBi with the help of the directors. After the gain improvement of the proposed antenna, its S11 and axial ratio (AR) characteristics are still in the desired range. The resonance frequency of the antenna is slightly shifted from 1700 MHz to 1705 MHz whereas AR remains below 3dB level successfully.

Comparison of Different Machine Learning Methods for Classification of Soil Horizons

Zülküf GümanZülküf Güman ,Hakan Tekin ,Berhan Aksakal ,Yasemin Gültepe — 2024-12-01

XSoil depth is critical for eco-hydrological modeling, carbon storage calculation and land evaluation. How to estimate soil depth over a large area of complex terrain with a limited number of sparse samples remains a challenge. The main aim of this study is to comprehensively compare machine learning algorithms to predict soil depth based on the relationship between soil properties. For this purpose, various models were created and compared in experiments using well-known performance measures such as accuracy, sensitivity and specificity. This study soil depth distribution map is thought to be useful for future applications, especially for places where soil depth data is not available. Classification of soil horizons has successfully performed the classification using these features for this problem.

ANN Analysis and ODE Model of Selected Factors Affecting Mental Health Problems of Children in Turkey

2024-12-01

In this study, the effects of selected variables on mental health problems in children were examined by using data obtained from Turkish Statistical Institute (TSI). A suitable environment for ANN analysis has been created by augmenting the data with minimum error by linear ordinary differential equation (ODE) system. The augmented data was analyzed through ANN and activation functions were expressed mathematically. Therefore, the effects of some pre-selected factors affecting mental health problems in children have been presented mathematically. Additionally, predictions were made regarding the variables used. The results of the study were supported by graphics.

CNN Hyperparameter Optimization for Customer Emotion Classification Based on Voice Recording Analysis

Taner HacıoğluTaner Hacıoğlu ,Sina Apak — 2024-12-01

Call centers play a crucial role as communication channels for companies providing customer services, and efficient management of incoming calls is essential for customer satisfaction and company performance. The use of data analytics and artificial intelligence techniques for evaluating and improving call center performance is increasingly prevalent. This article presents a detailed analysis of a call center dataset belonging to Gurmen Textiles, a company specializing in textile products with a strong focus on customer service. The company utilizes its call center to address customer inquiries and aims to maximize customer satisfaction. In this study, a deep learning model was developed using the call center dataset of Gurmen Textiles to differentiate between irate and normal calls. Irate calls may indicate customer dissatisfaction or issues, while normal calls typically encompass routine customer requests. Accurate classification of irate and normal calls can assist the company in managing customer relationships and enhancing service quality. By leveraging data analytics and deep learning techniques, analyses of call center data can be conducted more efficiently, leading to positive impacts on customer satisfaction. Speech analysis, a commonly employed technique in such analyses, can provide valuable insights into customer emotional states or concerns by evaluating audio recordings in call center data. The proposed approach achieves compatible results in SAVEE public access dataset with %98 and significant result in Gurmen Textiles dataset with %97 accuracy of classification.

The Effect of Using Mobile Applications on Science Teacher Candidates’ Achievement in Teaching Solutions Subject

Adem KenanAdem Kenan ,Recep Polat ,Recep Öz — 2024-12-01

The aim of this study is to examine the effect of Mobil Chemistry Laboratory Application – KimLab – designed and developed by the researcher in order to support teaching Solutions subject in Chemistry Laboratory course, on Science Teacher Candidates’ achievment. For this aim, a research was conducted with 94 science teacher candidates. A quasi-experimental research design with pretest-posttest control and experimental group as a qualitative research was used.. In order to evaluate academic achievement, Solutions’ Academic Achievement test developed by Simsek (2007) was used as both pretest and posttest for data obtaining. Reliability coefficient of the test including 30 multiple choice questions was determined as 0.76 (Cronbach Alpha). Data collected after pretest and posttest were analyzed with independent samples t-test. As a result of the research, there is a significant difference between control and experimental group in favor of the experimental group. It is founded that use of mobil technologies contributes achievement. Literatue review shows that there are similar conclusions with this research. In order to make the use of mobile technologies widespread in the field of education, more comprehensive studies are recommended.

Determination of Road Transport Emissions for Kocaeli Province

Abdullah ZorluAbdullah Zorlu ,Mustafa Özcan — 2024-12-01

In order to mitigate the catastrophic effects of the climate crisis, it is imperative to curtail greenhouse gas (GHG) emissions and the increase in global average temperature should be limited to 1.5°C if possible. Globally, cities are responsible for 70% of GHG emissions. Emissions from transportation in cities constitute an important source of these emissions. Approximately 95% of Türkiye’s transportation-related GHG emissions come from roads. Determining the amount of GHG emissions calculated in accordance with international standards at the city scale can contribute to emission reduction efforts. In this study, emissions from road transportation were calculated for Kocaeli Province between 2015-2021. The GHG emission results obtained by using two different calculation methods within the framework of the Intergovernmental Panel on Climate Change's guideline Tier 1 approach were compared and the amount of GHG emissions per capita from road transportation was calculated. Although GHG emissions from road transportation in Türkiye have increased over the years, the amount of emissions from road transportation has decreased in the 2015-2021 period for Kocaeli Province. In 2015, the calculated emission amount was 2,964.17 kt CO2-eq. In 2021, this amount decreased by 2.31% and was calculated as 2,895.65 kt CO2-eq. The ratio of Kocaeli's average road transport related emissions between 2015-2020 to Türkiye’s average transport related GHG emissions between 2015-2020 is 3.71%, and the ratio of Kocaeli's average road transport related GHG emissions to Türkiye’s average transport related GHG emissions between 2015-2020 is 3.99%.

Autumn Color Determination of European Cranberrybush (Viburnum opulus L.)

Büşra OnayBüşra Onay ,Candan Kuş Şahin ,Sariyya Rahimbayli — 2024-12-01

Autumn coloration is a phenomenon that affects the green leaves of certain deciduous trees and shrubs by which they transform into a wide range of shades of colors. However, the perception of those phenomenological transactions is an important criterion for successful landscape design practices. This study deals with European cranberrybush (Viburnum opulus L.) phenology assessment at the scale of individual plants and describes our current understanding in terms of autumn coloration. To accomplish this objective, selected European cranberrybush was monitored, photographed and selected properties measured during peak time in autumn. The visually perceived colors of leaves in European cranberrybush have also been verified, using spectrophotometric (colorimetric) and chlophyll (SPAD number) measurements. The leaf senescence was visually revealed and 10 representative leave samples were ordered according to their appearance from green to deep red. The SPAD numbers of ordered leaves were found to be in the range from 31.40 (sample 1) to 8.38 (sample 10) which closely correlated with visual perceptions. The highest redness color value of a: 25.79 metric was found with sample 10, while all leaves showed only different shades of yellowness (+b values) rather than blueness (-b). The discolorations were also evident by hue angle (Hue, ho), which it continuously lowered from 1220 greenness-yellowness (sample 1) to 22.320 redness-yellowness in color circle (sample 10). However, a close relationship was found between SPAD and Lightness (L); SPAD and yellowness-blueness (b*) but an inverse relationship between SPAD and greenness-redness (a*) of leaves. The CIE tristimulus (XYZ) values show some variations and are not well consistent with SPAD.

Mechanical Design of Aircraft Parts and Components

Berces KurtBerces Kurt — 2024-12-01

In the aviation industry, it is vital for aircraft safety that an aircraft meets the requirements under the airworthiness directives. In this context, in order for an aircraft to be suitable for continued airworthiness, the characteristics of the components or parts that make up the structure of the aircraft, such as strength, mechanical properties, durability and life limits, should be determined very meticulously and systematically. One of the most important stages when manufacturing an aircraft is the design process of the aircraft and its parts. The slightest defect or mistake to be made in the design process of a part in the aviation industry can cause serious accidents, injuries or loss of life during the active working process of that part. Since airplanes are constantly exposed to certain forces and loads, the parts or components designed for use in airplanes must be resistant to forces such as impact, pulling, tension and fatigue. In this review article, the vital importance of the design process and the design processes in parts or components used in aircraft are mentioned. In this study, the different design models on aircraft and parts in the literature and the analysis of these design models were compiled and evaluated, and thus it was aimed to create a resource for future studies in these areas.

Application of K-Means Clustering Algorithm to Weight Measurement System in a Series Production Line

Duran Arif GöçerDuran Arif Göçer ,Mete Kalyoncu — 2024-12-01

In this study; A weight measurement system has been developed in order to control the weights of heavy-duty air compressors after assembly and to record the weight data. In particular, air compressors are widely used to produce compressed air in heavy vehicles. Many electronic and mechanical products on the vehicle, especially the brake systems, work with compressed air. The interface was designed and the program was developed to keep the data and process under control in the serial production line compressor weight measurement system. C# programming language and SQL database were used to manage data traceability. Production work order forms form the basis of the weight recording system. Work order numbers prepared according to the production plan are unique and once the work orders are completed, the work order is not created again with the same barcode number. The data was recorded in the SQL database and the weight data of each compressor was matched with the work order barcode number and compressor code. The weight ranges of each compressor were determined with the first 100 compressors using the learning mode. Automatic comparison of weights was calculated in the k-means clustering algorithm and the approval-rejection stage was completed by checking the reference range limits of the compressor. Using the learning mode in the system provides optimum minimum-maximum It provided an advantage in determining the weights. Performance tests are applied to the compressors whose assembly is completed before the weight measurement stage. In addition to performance tests, product weight measurements will prevent possible errors and increase efficiency and quality in production. The purpose of ensuring traceability of data is to diagnose errors that may occur with the creation of a dataset. Resolving errors is one of the main factors in producing products with high quality and efficiency.

Effect of High Temperature Application on Mineral Matter Levels, Fatty Acids and Protein Fractions of White Cheese

Canan KayaCanan Kaya ,Salih Özdemir — 2024-12-01

The aim of this study was to increase the yield of white cheese by applying high temperature to milk. The average yield was determined as 17.58% in high-temperature white cheese samples and 16.40% in the control white cheese sample. During ripening periods, the average degree of ripening in cheese samples treated with a high temperature and control cheese samples was determined as 17.3% and 18.3%, respectively. It was determined that the average butyric acid amount (1.743%) white cheese samples treated with a high temperature was higher than that of the control samples (1.589%). The reason for the low rate of free fatty acids in this study may be due to the inactivation of the natural lipase enzyme of milk as a result of the high temperature applied to milk. It was determined that as the ripening times increased, the amount of both ?-casein and ß-casein decreased, whereas the protein degradation products increased.

Unsupervised Learning Methods and Application in Adult Census Income Dataset

Ayla SaylıAyla Saylı ,Emel Uğurlu — 2024-12-01

Unsupervised Learning is a data analysis technique used to explorer latent structure in data. Unsupervised Learning is the process of concentrating together objects with similar properties and grouping different ones without supervising the elements of the data. In this study, K-Means, DBSCAN and BIRCH clustering algorithms, which are unsupervised learning methods, were applied to the Adult Census Income dataset with 14 attribute and the target attribute is the annual income target attribute on Jupyter Notebook using Python3. In general, this dataset was used for classification purposes according to the target attribute based on its values which are less than 50 thousand dollars (0, zero class) or not (1, one class). However the annual income may not be able to give the similar groups of people. The aim of this study is to find these groups not only based on the annual income, considering all the attibutes in the dataset, comparing the performances of the clustering algorithms to observe the effects of the optimal number of clusters on the results. We first preprocessed this dataset and named as the Preprocessed Dataset and then we solved the balancing problem in this preprocessed set by the SMOTE method and named as the SMOTED_Preprocessed Dataset. After applying the algorithms to the datasets, 2 and 3 clusters are found and the results of the clusters are evaluated and the features determining the clusters were interpreted.

Spectroscopic, Thermodynamic, Electronic, HOMO-LUMO, MEP and NLO Analyses of 3-(Benzyl)-4-(3-Acetoxy-4-methoxybenzylideneamino)-4,5-dihydro-1H-1,2,4-triazol-5-one

Songül BoySongül Boy ,Gül Kotan ,Haydar Yüksek — 2024-12-01

In this work, a triazole derivative that is commonly employed in industry, healthcare, particularly the pharmaceutical sector, was theoretically analyzed. The spectroscopic, electronic, thermodynamic, HOMO-LUMO, MEP and NLO properties of molecule were calculated. The molecule optimized with 6-311G(d,p) basis set and DFT(B3LYP) method. The structural properties such as dipole moment (µ), the total energy, electronic properties (electron affinity, molecular softness, ionization potential, molecular hardness and electronegativity), thermodynamic of molecule were determined. The infrared (FT-IR) vibrational data of molecule were determined with Veda 4 software set. The 1H and 13C NMR calculations spectral data were obtained using the GIAO approach in the same basis set and than chemical shift values were compared with experimental data in literature. Also, the surface map such as MEP were obtained and thus, the electrophilic and nucleophilic moieties were shown on the compound. The nonlinear optical properties (NLO) were investigated and the results were evaluated according to the urea reference. Finally, the spectral and structural data of 1,2,4-triazole derivative compound were obtained and assessed in this theoretical study.

Probiyotiklerin Enkapsülasyonunda Kullanılan Teknikler

Hamza GoktasHamza Goktas — 2024-12-01

Sağlıklı beslenmeye olan talep her geçen gün giderek artmaktadır. Probiyotikler insan sağlığına faydalı birçok etkileri bulunmaktadır. Bu nedenle probiyotik tüketimi giderek artan bir popülerlik kazanmıştır. Bununla birlikte probiyotik tüketimindeki en önemli sorunlardan biri yeterli sayıda probiyotiklerin uzun canlılığının devam ettirilmesidir. Probiyotiklerin mide asitliğine dirençli olması, safra tuzu toleransı ve bununla birlikte bağırsak epitel hücrelerine tutunma gibi bazı önemli özellikler sahip olması arzu edilmektedir. Bu kapsamda çeşitli enkapsülasyon teknikleri geliştirilerek probiyotiklerin bağışıklık sistemi boyunca canlılığının sürdürülmesi hedeflenmiştir. Bu amaçla püskürterek kurutma, dondurarak kurutma, emülsifikasyon, ekstrüzyon, elektrospinning gibi bazı enkapsülasyon teknikleri kullanılmıştır. Bu çalışmada probiyotikler, seçim kriterleri, probiyotiklerin uzun süre canlılığını koruma amacıyla uygulanan enkapsülasyon teknikleri ve kullanılan enkapsülasyon materyalleri hakkında detaylı bilgi sunulmuştur.

Cryptocurrency Sentiment Analysis with Topic Modeling Methods

Gül Cihan HabekGül Cihan Habek ,Mansur Alp Toçoğlu ,Aytuğ Onan — 2024-12-01

Cryptocurrencies designed to be used on the internet have attracted the attention of many people since the day they were released. As the number of people turning to cryptocurrency trading increased, the number of texts on the subject also increased and the need to analyze the texts, which turned into a large data set, arose. There are analysis studies in the literature using tweets taken from the Twitter platform on different topics. This study aims to perform sentiment analysis on cryptocurrency-tagged tweets shared on the Twitter platform, in line with the rising popularity of cryptocurrencies and the interest of internet users in this field. Since Twitter is a popular social media platform that offers a wide data set, tweets containing users' emotional expressions about cryptocurrencies constitute the main data source of this study, and a data set specific to the study was created by automatically tagging tweets with LSTM. Feature detection was made with LDA and NMF algorithms, which are topic modeling methods; Models were created with NB, LR and SVM algorithms and results were obtained for classification accuracy and f1-score metrics. When the results were compared, the highest success was achieved when the LDA method and SVM classifier were used, and 87.89% accuracy and 87.85% F1-criterion values were reached in the model created by setting the subject feature size as 500 and the number of components as 40. The best result obtained with the NMF method was 87.43% accuracy and 87.34% F1-criterion with the model created with the SVM classifier when the subject feature size and the number of components were 500 and 50, respectively. The second-best performance was obtained with the LR classifier for both methods; With the LDA method, 87.13% accuracy was recorded when the subject feature size and the number of components were selected as 300 and 40, and 87.13% F1-criterion values were recorded when 500 and 30 were selected, respectively, while with the NMF method, 87.01% accuracy and 86.90% F1-criterion values were reached when the subject feature size was 500 and the number of components was 50. The lowest performance was obtained with the NB classifier for both methods. The NB classifier showed its best performance with 73.00% accuracy and 73.01% F1-measure values with LDA when the subject feature size and the number of components were selected as 200 and 50, respectively, and 70.00% accuracy and 70.09% F1-measure values with NMF when 500 and 40 were selected. According to the results, both LDA and NMF methods generally showed successful performance. The highest and most consistent performances were obtained with the SVM classifier in both methods, and it was concluded that the SVM algorithm was the most successful classifier compared to the other two classifiers used. In addition, the findings obtained from the study show that emotional expressions related to cryptocurrencies on Twitter can be analyzed effectively.

Design of Gold Nanoparticle/quercetin Modified, and Nano-channeled SF/PLGA Film Scaffolds for the Oriented Growth of Sensory Neurons

İlyas Özçiçekİlyas Özçiçek — 2024-12-01

Nerve injuries, which are very common around the world, have dramatic effects on life quality of the patients. There is a great need for alternative and innovative regenerative approaches, since a very limited level of regeneration can be achieved with current therapies. In the field of nerve tissue engineering, intensive efforts are being made to develop biocompatible and conductive nerve guidance channels with topography that can guide the linear orientation of damaged axons. The aim of this study is to develop hybrid SF/PLGA film scaffolds with nano-channeled topography and decorated with quercetin-conjugated gold nanoparticles (AuNP-Q) and to investigate the behavior of DRG (dorsal root ganglion) sensory neurons primarily isolated from the mice on these materials. The using hybrid film scaffold increased the physical and mechanical properties of the material. The laminin coating promoted the cellular adhesion and growth. Nano-channeled scaffolds modified with nanoparticles (SF/PLGA G0.5-AuNP83-Q) highly promoted the axonal orientation. It was evaluated that the developed biomaterial could help the orientation of cellular neurites/axons and support regeneration after implantation in a nerve injury site, within the scope of potential in vivo nerve tissue engineering studies.