Sürdürülebilir Kalkınma Amaçları
Yazarlar
- Yildirim, Ozal(10)
- Bulut, Hasan(9)
- Atangana, A(8)
- Baskonus, HM(8)
- Bulut, H(8)
- Demir, Yakup(8)
- Hammouch, Z(8)
- Karakose, Mehmet(8)
- Mekkaoui, T(8)
- Baskonus, Haci Mehmet(7)
- Yilmazer, Merve(7)
- Orhan, Ahmet(5)
- Agoujil, S(4)
- Aslan, Serpil(4)
- Baloglu, Ulas Baran(4)
- Belgacem, FB(4)
- Boydak, Merve(4)
- Caliskan, Abuzer(4)
- Cattani, C(4)
- Celik, Erkan(4)
- Kaya, Mehmet(4)
- Talo, Muhammed(4)
- Çil, Zeynel Abidin(4)
- Özceylan, Eren(4)
- Barros, J.(3)
- Benedicenti, L.(3)
- Cachim, P.(3)
- Drobne, S.(3)
- El Naggar, H.(3)
- Eristi, Belkis(3)
- Eristi, Huseyin(3)
- Gunay, Mihriban(3)
- Kljajic Borstar, M.(3)
- Koseoglu, Murat(3)
- Mete, Süleyman(3)
- Sivasundaram, S(3)
- Uyanik, Hakan(3)
- Yalcin, Beyza Furtana(3)
- Yilmaz, Mehmet(3)
- Zadnik Stirn, L.(3)
Yayınlar
- BildiriYalnız künye2026Scopus SJRQ4Residual-Based Evaluation of Basin and Basin Edge Effects in Türkiye: Implications for Regional Site Amplification Modeling
İçen, Abdullah; Ilki, A.; Goksu, C.; Cinar, T.; Erdik, M.
This study investigates basin, basin edge and complex topography effects in ground motion records using residuals from empirical ground motion models, to better understand regional site amplification patterns in Türkiye. Using data from the updated Strong Motion Database of Türkiye (SMD-TR) strong-motion database, we compute residuals using site amplification function and analyze their spatial distribution across regions. The clustering technique, Gaussian Mixture Models (GMM), is employed to identify regions with coherent residual patterns, primarily to capture regional site effects. After regionalization, peak amplifications within the VS30 range of 400–550 m/s are examined for potential associations with sedimentary basins or basin edge effects. The results reveal systematic amplification anomalies within the VS30 range of 400–550 m/s that are not fully captured by conventional site proxies such as VS30. Notably, residuals consistently cluster along basin edges and within deep valley regions, indicating the influence of complex three-dimensional and topographic amplification mechanisms. These observations underscore the limitations of current site characterization approaches and motivate further refinement of site response models, particularly for partially non-ergodic applications in seismic hazard analysis. This study focuses on residual-based evaluations to identify potential basin, basin-edge, and topographic effects that warrant more detailed investigation.
- BildiriYalnız künye2026LLM Based Software Test Scenario Generation System From Bug Descriptions
Gundem, Merve Yilmazer; Karakose, Mehmet
Large, user-interactive software systems require extensive software testing for safe deployment. Manually generated test cases by software testers require extensive time and effort, and their scope remains limited. In this study, a system based on LLM is proposed for automatic test cases and test steps generation from bug descriptions. In the proposed system, bug descriptions were first collected from the Defects4J Dataset. Keywords relevant to the bug scope were extracted from the descriptions. Structured automatic prompts were generated from the extracted keywords. The generated prompts were fed into the LLM as input, and software test cases appropriate for the bugs were generated. The method was evaluated on actual bug descriptions by comparing it to a general (baseline) prompt. Experimental results show that the lengths of the generated scenarios are similar, but the proposed method increases the keyword match rate with error descriptions to an average of 89 %. The findings reveal that keyword-driven prompts are effective in generating more reliable and error-specific test scenarios without requiring additional data.
- BildiriAçık Erişim2026Scopus SJRQ4Investigation of Surface Leakage Currents of High Voltage Insulators Under Different Pollution Conditions at Power Frequency AC Voltage
Külekçioğlu, Abdülcelil; Çetin, Muhammed Sefa; Gülan, Harun; Özdemir, Mahmut Temel; Gençoğlu, Muhsin Tunay; Cebeci, Mehmet; Moldovan, L.; Gligor, A.
In this paper, the electrical behavior of high voltage insulators under different pollution conditions has been investigated experimentally at power frequency (50 Hz) alternating voltage. The main objective of the study is to reveal the effect of pollution layers on the surface leakage currents and flashover voltages of insulators. For this purpose, a climatic chamber with controllable temperature and humidity conditions, together with a 200 kV high voltage test system, was employed. Three types of insulators (standard porcelain, fog-type porcelain, and silicone composite) were tested, and artificial pollution layers of varying densities were applied to their surfaces in accordance with standard procedures. During the experiments, the surface leakage currents were recorded, and flashover voltages were determined by gradually increasing the applied voltage. The obtained data enable comparative analyses for different pollution levels and insulator types. The originality of this study lies in providing a comprehensive dataset through numerous experiments under controlled laboratory conditions, thereby addressing gaps in the existing literature. This dataset establishes a solid foundation for the modeling of insulators electrical behavior and for the development of new approaches in future research.
- BildiriYalnız künye2025Scopus SJRQ1Perceived learning after manual airway clearance techniques practice: peer learning vs. simulation-based learning
Cakmak-Onal, Aslihan; Aksel-Uylar, Aydan Asli; Saglam, Melda; Topcuoglu, Ceyhun; Vardar-Yagli, Naciye; Calik-Kutukcu, Ebru; Demirel, Aynur; Inal-Ince, Deniz
- BildiriAçık Erişim2025LLM-Based Video Analytics Test Scenario Generation in Smart Cities
Yilmazer, Merve; Karakose, Mehmet
Rapid advances in the field of artificial intelligence have made significant contributions to the automation of software development and testing stages. Software created for use in various fields is tested with test scenarios created manually by software test experts or using test automation. Testing large-scale software with these methods complicates the testing phases because it requires increased human intervention and includes complex applications. In this study, an LLM-based scenario generation framework enhanced with prompt engineering is proposed for testing software to be used for video analysis in smart cities and smart campus areas. Thus, software test scenarios are created by strengthening large language models that are fast, flexible and have high learning ability using prompt engineering techniques. Test scenarios produced through LLM reinforced with prompt engineering techniques were evaluated with rarity and reality metrics and it was determined that more robust scenarios were produced compared to randomly generated test scenarios in the relevant field.
AtıfScopus3OpenAlex3
İncele - BildiriYalnız künye2025Scopus SJRQ4A Hybrid Machine Learning Architecture for Acoustic Fault Diagnosis in Electric Motors: Offline and Online Modes via Mobile Phone
Yordanov, Nikolay; Zhilevski, Marin; Mikhov, Mikho; Ertargin, Merve
This paper proposes a hybrid ML architecture for non-invasive acoustic fault diagnosis in electric motors and machines using a.NET MAUI cross-platform mobile app for short audio capture (5-20s), audio-to-image transformations and dual-mode inference: embedded ML.NET/ONNX models for offline preliminary detection and Azure ML-hosted deep CNN/RNN for online probabilistic multi-fault analysis. The primary objective of this paper is to demonstrate the benefits of the proposed architecture and to show the specific tools which can be used for implementation. Architectures (embedded, cloud, hybrid) are evaluated by listing main advantages and disadvantage, highlighting hybrid approach that combines offline resilience, reduced latency and keeping the possibility for complex analysis by using online hosted model. Sample mobile application design is added to showcase the proposed user experience.
- BildiriAçık Erişim2025Toplam Elektron Içeriǧi ve Uzay Iklim Verileri Kullanilarak Transfer Öǧrenme ile Deprem Öncüllerinin Analizi
Uyanik, Hakan; Şentürk, Erman; Köküm, Mehmet; Özçelik, Salih Taha Alperen; Akpinar, Muhammed Halil; Şengür, Abdulkadir
The stress changes occurring in the Earth's crust before earthquakes may cause some anomalies in the ionosphere and cause significant changes in the Total Electron Content (TEC) and Space Climate (SCC) parameters. The analysis of these changes offers a significant potential in the detection of earthquake precursors. In this study, the analysis of earthquake precursors was performed using TEC and UC data and the effectiveness of transfer learning and machine learning techniques was investigated. Features were extracted from five-day TEC and UC data using a pre-trained Multi-Input One-Dimensional Convolutional Mixer Network (ÇG-TB-EMA) and these features were classified with different machine learning algorithms. Within the scope of the study, earthquakes with a magnitude of Mw≥6.0 that occurred during the low and high solar activity periods of the 24th solar cycle were analyzed. The earthquakes were selected from the Far East region between 10°S-45°N latitudes and 120°E-160°E longitudes, where intense seismic activity is observed. Experimental studies have shown that during the low solar activity period, support vector machines (SVM) and artificial neural networks (ANN) models exhibited the highest performance with 96.04% accuracy and 96.08% F1 score, respectively. During the high solar activity period, the classification success decreased slightly, and SVM and ANN models achieved the best results with 90.28% accuracy and 89.86% F1 score. These findings reveal that the effect of solar activity on ionospheric variables should be taken into account in earthquake precursor detection. For future studies, long-term analyses are recommended to better understand the effects of solar activity.
- BildiriAçık Erişim2025Investigation of Mechanical Properties of Hot Mix Asphalt Modified with Elvaloy and Polyphosphoric Acid
Yilmaz, Mehmet; Yalcin, Beyza Furtana; El Naggar, H.; Barros, J.; Cachim, P.
In order for the bituminous hot mix layers to resist the increased traffic load and volume as well as the adverse environmental conditions for a long time without being deformed, the bituminous binder used in the mixture has been commonly modified with the Styrene-Butadiene-Styrene (SBS) polymer. The high demand for SBS makes it difficult to supply and therefore brings on the use of alternative additives. In this study, the combined use of Elvaloy and phosphoric acid (PPA) was investigated. Additionally, the effects on the mechanical properties of bituminous mixtures were examined. Modified binders were prepared using Elvaloy/PPA additives mixed under specified conditions. Hot mix asphalt (HMA) samples prepared with these binders were subjected to Marshall stability and flow, indirect tensile stiffness modulus, and indirect tensile fatigue tests to comprehensively evaluate the effects of modified bi nders on the mechanical properties of bituminous hot mixtures. As a result, the bituminous hot mix tests revealed that asphalt mixtures improved their mechanical properties at the specified ratios.
- BildiriAçık Erişim2025Deep Learning-Based Approaches for Short-Term Residential Electricity Consumption Prediction: A Comparative Study of LSTM, CNN-LSTM, and CNN-GRU Models
Gunay, Mihriban; Yildirim, Ozal; Demir, Yakup; Benedicenti, L.; Liu, Z.
The rapid increase in the world population has increased electricity consumption and demand for electricity. In order for electricity distribution companies to determine electricity demand, household electricity consumption must be predicted. In this study, an open access dataset was used to predict individual household electricity consumption. For this purpose, deep learning-based LSTM, CNN-LSTM and CNN-GRU models were developed and a comparative analysis was performed. The performance of the models was evaluated using four evaluation metrics commonly used in prediction model performance evaluation. According to the results obtained, it was seen that the models used in the study were successful in predicting short-term electricity consumption. In particular, the CNNLSTM model was found to have a lower error rate compared to the other two models.
- BildiriAçık Erişim2025Health Status Classification of Electric Motors Using CNN-Based Models and SDP Images Under Varying Noise Conditions
Ertarğın, Merve; Yordanov, Nikolay; Zhilevski, Marin; Benedicenti, L.; Liu, Z.
The increasing application of electric motors across various industrial sectors requires effective monitoring and early diagnostics to prevent potential failures. This study explores the use of Convolutional Neural Network (CNN)-based models together with Symmetrized Dot Pattern (SDP) sound representations for classifying the health status of electric motors under different noise conditions. Acoustic data from a brushless DC motor were transformed into SDP images, which were then used to train CNN models. The dataset included recordings of motors in “Good”, “Broken” and “Heavy Load” conditions, captured under various noise environments such as pure, talking, white noise, atmospheric, and stress test conditions. The classification tasks were conducted under three conditions: assessing motor health status, evaluating both motor health status and noise types, and excluding the stress test noise type for a balanced dataset. The results demonstrated that the CNN models achieved high accuracy rates in classifying motor health status, with the Custom CNN model performing best in simpler tasks and MobileNet excelling in more complex scenarios. The study highlights the feasibility of using SDP images with CNN-based models for fault classification in motors and suggests future research directions for improving classification accuracy through advanced feature extraction techniques and multimodal data representations.
AtıfScopus1İncele - BildiriAçık Erişim2025Investigation of Fatigue Performance of Hot Mix Asphalt Mixtures Modified with Elvaloy and Phosphoric Acid (PPA) as an Alternative to SBS Polymer
Furtana-Yalcin, Beyza; Ozaslan, M.
In this study, the combined use of Elvaloy and phosphoric acid (PPA) was investigated to enhance the performance of asphalt concrete (Hot Mix Asphalt - HMA) layers used in flexible pavement structures under increasing traffic loads and environmental conditions. These additives, considered as alternatives to Styrene-Butadiene-Styrene (SBS) polymer, were evaluated solely for their effects on the mechanical performance of HMA mixtures. Binders modified with Elvaloy/PPA at specified proportions were used to produce HMA specimens, and indirect tensile fatigue tests were conducted on these samples. The experimental results indicated that the addition of Elvaloy and PPA improved the mechanical properties of mixtures prepared with bituminous binders, extended fatigue life, and enhanced the strength and deformation resistance of asphalt mixtures at the selected proportions. The findings suggest that the Elvaloy/PPA additive combination could be a viable alternative to SBS polymer and has the potential to extend the service life of hot mix asphalt. Thus, it was concluded that the use of these additives contributes to improving HMA performance and supports sustainable pavement design.
AtıfOpenAlex1İncele - BildiriYalnız künye2024Exploring Triboelectric Properties in Sputtered Si(100)/SiO2/Cr/ZnO Thin Films - Ag/FEP Using Contact Speration Technique for Renewable Energy Harvesting
Yüzüak, Gizem Durak; Benhala, B.; Raihani, A.; Qbadou, M.
Currently, there is an ongoing inquiry into triboelectric nanogenerators (TENG), which use the triboelectric phenomenon to capture electrical energy from mechanical vibrations. The work aims to figure out the magnetron sputtered Cr/ZnO-based films on SiO2/Si(100) substrates with the construction of a Cr/ZnO-Ag/FEP tribo-based nanogenerator (TENG) and a specific emphasis on measuring TENG properties. The structural investigation of these films was conducted using X-ray diffraction (XRD) photoluminescence (PL), attenuated total reflectance fourier-transform infrared spectroscopy (AT-FTIR), and current - voltage (I-V) analysis in contact-separation mode (CSM). The Cr/ZnO-Ag/FEP TENG has a notable instantaneous maximum output voltage of around 63 V and a current density of 7 μA/cm2 when compared to other nanogenerators based on semiconductors, as determined by a comparative analysis. The results of this study provide useful information on the possible application of optimized magnetron sputtered Cr/ZnO-based films in triboelectric and energy harvesting systems, which might contribute to the progress of sustainable and renewable energy technologies.
- BildiriYalnız künye2024Performance Analysis of Attention Mechanisms for Melanoma Cancer Detection
Dogan, Gurkan
The deadliest type of skin cancer worldwide is melanoma. If melanoma skin cancer is detected and treated at an early stage, the mortality rate can be significantly reduced. In particular, convolutional neural networks, one of the deep learning techniques that have achieved remarkable success in recent years in areas such as computer vision, natural language processing, signal processing, etc., can detect this type of cancer automatically, quickly and with high accuracy. Moreover, the accuracy performance of the neural network can be further increased by using attention mechanisms together with convolutional neural networks. However, attention mechanisms have not been used correctly and effectively in the neural network models proposed so far for the detection of melanoma skin cancer. This study examines the effect of attention mechanisms on the accuracy performance of a convolutional neural network and guides researchers to use these mechanisms correctly and effectively. To achieve this, a base model based on a convolutional neural network was built. Then, seven different attention mechanisms were integrated separately into this basic model and the results of the experimental tests of each were presented.
AtıfScopus1OpenAlex1İncele - BildiriYalnız künye2024Scopus SJRQ4Constructing the Criteria in Determining the Product Groups for Agriculture 4.0 Applications
Erdoğan, Melike; Konurhan, Zekeriya; Yücesan, Melih; Gül, Muhammet; Mirzazadeh, A.; Molamohamadi, Z.; Erdebilli, B.; Babaee Tirkolaee, E.; Weber, G.-W.
With Agriculture 4.0, the use of techniques such as sensors, robots, artificial intelligence, and machine learning in agriculture has started. It is aimed to increase productivity in agriculture by reducing food loss and waste through Agriculture 4.0. It is a critical decision to determine which products should be handled first for Turkey to benefit from the advantages of Agriculture 4.0 as soon as possible compared to developed countries in the field of agriculture. At this point, the problem of which factors should be addressed in the determination of product & product groups arises. To handle this, in this study, a multi-criteria analysis has been applied to prioritize the factors that should be considered in the determination of the critical fruit and vegetable group for export, which should be considered as a priority within the scope of Agriculture 4.0. In this context, a multi-criteria analysis has been carried out by adopting the Bayesian Best Worst Method BWM (B-BWM), which is an improved version of a pairwise comparison-based BWM method and applied to group decisions. As a result of the analysis, the most important and least important criteria to be used in determining which products or product groups are more suitable for Agriculture 4.0 applications in Turkey and which should be invested in priority have been determined.
- BildiriAçık Erişim2024A New Hybrid CNN Model for Abnormal Behaviour Detection in Smart Campus
Yilmazer, Merve; Karakose, Mehmet; Ay, Berhan Turku
Security and monitoring systems are provided through video cameras in public places such as universities, hospitals, cinemas and theater halls. In smart campuses created by the integration of various sensors and digital technologies, there are many video cameras indoors and outdoors in the campus areas to ensure the safety of students, academics, administrators and visitors. Examining the images recorded with these video cameras in terms of security and detecting abnormal behaviours requires a long time and effort. In this study, a new hybrid convolutional neural network (CNN) model is proposed to detect abnormal behaviors occurring in smart campus areas. In the proposed method, firstly, the features of the UTI public dataset, which includes various student behaviors, are extracted with the hybrid use of InceptionV3 and MobileNetV2 deep transfer learning models. The extracted features of the images were combined and classified by converting them into a single feature vector. 6 different behavior classes, 3 of which are normal and 3 of which are abnormal, have been classified and detected with 92.7 % accuracy. Experimental results show that the proposed method for abnormal behavior detection produces more efficient results than other methods in terms of feature extraction, accuracy and computational costs.
AtıfScopus2OpenAlex1İncele - BildiriAçık Erişim2024A Deep Learning-Based Approach for Damage Detection in Cultural Heritage Images and Generating Heat Maps
Ogdu, Cagatay Umut; Yilmazer, Merve; Karakose, Mehmet
Cultural structures may be damaged and damaged over time due to external factors such as natural and social disasters, which may lead to the loss of national assets if cultural assets are not protected. Therefore, the protection of cultural heritage is of great importance not only for today's generations but also for future. In recent years, deep learning and image processing have made great progress and have become very effective for detecting and analyzing patterns. These techniques can also be used to solve complex problems such as damage detection of structures in the field of cultural heritage. Deep learning methods can be used to determine the current status of structures and detect possible damage by working on images obtained from different angles of structures, while also reducing human errors. In this study, as an important step for the protection of cultural heritage, a detection method was developed using deep learning models on damaged and intact cultural heritage building images. The data set used in the study consists of damaged and intact cultural heritage building images. An attempt was made to determine the model that gave the best results by using pre-trained models with the Transfer Learning method. Heat maps were created using the Grad-CAM method to identify damaged areas on the trained models, thus making it possible to more clearly identify the damaged areas in the image. This method can play an important role in preserving cultural heritage. It can be used to detect damaged areas and direct them to repair.
AtıfScopus3OpenAlex4İncele - BildiriAçık Erişim2024A New Approach to Detecting Free Parking Spaces Based on YOLOv6 and Keyframe Detection with Video Analytics
Yilmazer, Merve; Karakose, Mehmet; Tanberk, Senem; Arslan, Samed
With urbanization and population growth, automobile use has also increased. As a result of this, there is a need to expand parking management and inspection systems to ensure safe and efficient use of parking areas. A new deep learning-based method has been proposed as an alternative to traditional systems to control free spaces in parking areas monitored by CCTV security cameras. A large amount of video data needs to be processed for parking areas recorded with video cameras 24 hours a day, 365 days a year. Repeating frames in videos cause overlearning in deep learning models. It also increases memory and computation cost. For this reason, keyframe detection based on sampling was first performed on the sample video of the Dragon Lake Parking (DLP) dataset and a summary of the dataset was obtained. Then, empty parking areas were detected by YOLOv6 deep neural network. It has been observed that the model can detect empty parking areas at a rate of 96.6% mAP.
AtıfScopus7OpenAlex5İncele - BildiriYalnız künye2024An Analysis of Dependence Relationships Between Covid-19 Pandemic and Environmental Conditions Cases Study in Indonesia, England, and Türkiye
Handhayani, Teny; Caglar, Durdane Kocacoban; Wibowo, F.W.
This study explores the causal relationships between environmental conditions and Coronavirus disease 2019 (COVID-19) using Bayesian networks in three geographically distinct cities: Jakarta (Indonesia), London (England), and Istanbul (Türkiye). The datasets collected from these cities span from January 2020 to October 2022, encompassing a variety of environmental variables (Particulate Matter (PM2.5), temperature, humidity, precipitation, wind speed, and Ultraviolet (UV) index) and COVID-19 variables (new cases, deaths, and population density). By using the PC algorithm and Greedy Equivalence Search (GES) to generate causal graphs, and applying the Intersection-Validation (InterVal) method to derive agreement graphs, this research reveals significant geographical differences in how environmental factors influence COVID-19 dynamics. The Peter-Clark (PC) algorithm identifies conditional dependencies, while GES optimizes the structure of the causal graph. The study highlights the significant impact of temperature, UV index, and humidity on COVID-19 dynamics, emphasizing the importance of localized public health strategies tailored to specific environmental conditions. These findings contribute to the understanding of environmental influences on COVID-19 and offer valuable insights for future research and policy-making aimed at mitigating the impact of pandemics in diverse geographical settings.
AtıfScopus3OpenAlex2İncele - BildiriAçık Erişim2024Investigation of the Effects of Different Modified Asphalts on Storage Stability
Yalcin, Beyza Furtana; Yilmaz, Mehmet; Yalcin, Erkut; Ozdemir, Ahmet Münir; El Naggar, H.; Barros, J.; Cachim, P.
In this study, the storage stability of modified bitumen prepared with bitumen additives styrene-butadiene-styrene (SBS) and American Gilsonite (AG) was evaluated. For this purpose, modified bitumen was prepared by using different ratios of each additive type. The prepared modified bitumens were subjected to storage stability test according to EN 13399 standard. Penetration, softening point and rotational viscometry tests were performed on the samples subjected to storage stability test. As a result of the penetration and softening point tests of the samples prepared with SBS after storage stability, it was determined that the penetration and softening point values of the samples obtained from the upper part of the tube were higher. This is thought to be due to the accumulation of SBS, which has a lower specific gravity than bitumen, in the upper part of the tube during the storage stability test. Considering the viscosity test results, it was determined that the viscosity values of the modified bitumen samples obtained from the upper part of the tubes were higher than the samples obtained from the lower part of the tubes.
- BildiriAçık Erişim2024Effect Of The Actıvatıon Force On The Healıng Level Of Asphalt Mıxture Wıth Embedded Capsules
Yalcin, Erkut; Yilmaz, Mehmet; Yalcin, Beyza Furtana; El Naggar, H.; Barros, J.; Cachim, P.
By increasing the asphalt mixtures' inherent capacity for self-healing, encapsulated rejuvenators can extend the service lives of asphalt pavements. The capsules are shattered when crack damage occurs, allowing the rejuvenator ingredient to seep into the cracks. In this work, a brand-new kind of capsule was created. Sunflower oil was a rejuvenator in these capsules. We looked at how well asphalt mixture samples with and without capsules self-healed under static loading. The capacity for self-healing of asphalt mixtures incorporating a novel class of capsules that contain sunflower oil as a rejuvenating agent. For this reason, the ability of the rejuvenating ingredient to diffuse inside of samples of virgin bitumen and the healing potential of combinations of cracked asphalt containing capsules have both been assessed. Due to the mechanical properties of the polymeric capsules examined. Then, using a unique mechanical test, the impact of capsule content on the crack-healing characteristics of asphalt mixtures was assessed at various healing durations (from 5 to 216h) and loads (from 0 to 85kN). As the applied load increases, the healing levels of the asphalt samples with capsules rise; later, the healing levels of the asphalt samples with capsule remain constant. According to the findings, the maximal healing level was typically attained after 96 hours and remained constant until 216 hours.
