Tarsus Üniversitesi Kurumsal Akademik Arşivi
DSpace@Tarsus, Tarsus Üniversitesi tarafından doğrudan ve dolaylı olarak yayınlanan; kitap, makale, tez, bildiri, rapor, araştırma verisi gibi tüm akademik kaynakları uluslararası standartlarda dijital ortamda depolar, Üniversitenin akademik performansını izlemeye aracılık eder, kaynakları uzun süreli saklar ve telif haklarına uygun olarak Açık Erişime sunar.
Güncel Gönderiler
Mechanical and Tribological Performance of Polypropylene/Tin Powder Composites
(Natl Inst Science Communication-Niscair, 2022) Gulesen, Mustafa; Yetgin, Salih Hakan; Unal, Huseyin
In this study, the effect of Tin powder filler content on the mechanical and tribological performance of Tin filled Polypropylene (PP) composites were investigated. Polypropylene composites were prepared in a Brabender kneading chamber. The melt was transferred to a laboratory hot press and compression molded into samples for tests. The mechanical performances of the polymer based composites were determined by tensile and notched izod impact tests. The tribological tests were carried out in dry condition using pin-on-disc at 0.5-1.5 m/s Sliding Speed (SS) and 10-30 N loads. The mechanical test results demonstrated that the incorporation of Tin powders increased the Tensile Strength (TS) (5.6%), tensile modulus (TM) (19.8%) and izod Impact Strength (IS) (41.8%) while decreased the Elongation at Break (EB) (80%) values of Tin powder filled PP composites. The Friction Coefficient (COF) and Specific Wear Rate (SWR) decreased with the increase in filler content. The COF of unfilled PP, PP-8% Tin powder, PP-16% Tin powder and PP-24% Tin powder composites decreased about 20%, 23.4%, 21.8% and 29.3% with the increase in applied load from 10 N to 30 N. The SWR of the Tin powder filled PP composites decreased by 91% compared to unfilled PP polymer at 1.5 m/s speed and 30 N load value.
Novel Schiff Base Sulfonate Derivatives as Carbonic Anhydrase and Acetylcholinesterase Inhibitors: Synthesis, Biological Activity, and Molecular Docking Insights
(Wiley-V C H Verlag Gmbh, 2025) Yasar, Umit; Demir, Yeliz; Gonul, Ilyas; Ozaslan, Muhammet Serhat; Celik, Gizem Gumusgoz; Turkes, Cuneyt; Beydemir, Sukru
Sulfonate derivatives are an essential class of compounds with diverse pharmacological applications. This study presents the synthesis and detailed characterization of six novel Schiff base sulfonate derivatives (L1-L6) through spectroscopic techniques (FT-IR and NMR). Their inhibitory potential was evaluated against human carbonic anhydrase isoenzymes (hCA I and hCA II) and acetylcholinesterase (AChE), which are crucial therapeutic targets for diseases such as glaucoma, epilepsy, and Alzheimer's disease. The KI values for the compounds concerning AChE, hCA I, and hCA II enzymes were in the ranges of 106.10 +/- 14.73 to 422.80 +/- 17.64 nM (THA: 159.61 +/- 8.41 nM), 116.90 +/- 24.40 to 268.00 +/- 35.84 nM (AAZ: 439.17 +/- 9.30 nM), and 177.00 +/- 35.03 to 435.20 +/- 75.98 nM (AAZ: 98.28 +/- 1.69 nM), respectively. Molecular docking analyses revealed key interactions within the active sites of the enzymes, including hydrogen bonding with critical residues and pi-pi stacking interactions. Notably, L3 demonstrated superior inhibition against hCA I (KI: 116.90 +/- 24.40 nM) and AChE (KI: 106.10 +/- 14.73 nM), positioning it as a promising lead compound. This comprehensive investigation contributes to the development of isoform-specific inhibitors for therapeutic use and provides valuable insights into their binding mechanisms. The findings underscore the potential of Schiff base sulfonates as scaffolds in drug discovery for neurodegenerative and metabolic disorders.
Onosma rutila (Boraginaceae) Plant Extract: Chemical Composition, Biological Analysis, and Film Forming Potential With Polymers
(Wiley-V C H Verlag Gmbh, 2025) Binzet, Riza; Binzet, Gun; Turunc, Ersan; Cevik, Pinar Kuce; Demir, Didem; Arslan, Hakan
This study aimed to integrate the ethanolic extract obtained from the aerial parts of endemic Onosma rutila as a new bioactive ingredient into polymeric films and to produce biofunctional composite thin films for use in biomedical applications. Initially, the definition of the plant extract was carried out in terms of chemical composition and biological activities. The main component of the extract was revealed as 9,12-octadecadienoic acid (15.90%). Antimicrobial activity was evaluated against a total of five microorganisms by the well diffusion method and microdilution technique. MIC99 results showed the lowest inhibition against Candida albicans, suggesting a stronger antifungal effect than antibacterial activity. Also, the 2,2-diphenyl-1-picrylhydrazyl scavenging activity of O. rutila, ascorbic acid, and butylated hydroxytoluene at 500 mu g/mL showed values of 87.63, 90.13, and 46.82%, respectively. In the next phase, the extract, which was revealed to be an effective biological agent, was incorporated into the polymer solutions prepared based on chitosan and polyvinyl alcohol at different ratios to produce a series of thin films. For application purposes, the thin films' chemical compositions, water retention capacities, and morphological properties were determined and their potential for use as wound dressing material was evaluated.
ANDClust: An Adaptive Neighborhood Distance-Based Clustering Algorithm to Cluster Varying Density and/or Neck-Typed Datasets
(Wiley-V C H Verlag Gmbh, 2024) Senol, Ali
Although density-based clustering algorithms can successfully define clusters in arbitrary shapes, they encounter issues if the dataset has varying densities or neck-typed clusters due to the requirement for precise distance parameters, such as eps parameter of DBSCAN. These approches assume that data density is homogenous, but this is rarely the case in practice. In this study, a new clustering algorithm named ANDClust (Adaptive Neighborhood Distance-based Clustering Algorithm) is propoesed to handle datasets with varying density and/or neck-typed clusters. The algorithm consists of three parts. The first part uses Multivariate Kernel Density Estimation (MulKDE) to find the dataset's peak points, which are the start points for the Minimum Spanning Tree (MST) to construct clusters in the second part. Lastly, an Adaptive Neighborhood Distance (AND) ratio is used to weigh the distance between the data pairs. This method enables this approach to support inter-cluster and intra-cluster density varieties by acting as if the distance parameter differs for each data of the dataset. ANDClust on synthetic and real datasets are tested to reveal its efficiency. The algorithm shows superior clustering quality in a good run-time compared to its competitors. Moreover, ANDClust could effectively define clusters of arbitrary shapes and process high-dimensional, imbalanced datasets may have outliers. This study proposes a new clustering algorithm named ANDClust to handle datasets with varying density and neck-typed clusters. In the proposed algorithm, an Adaptive Neighborhood Distance (AND) ratio is used to weigh the distance between the data pairs as if it differs for each data pair in the dataset. This method makes the approach support not only the varying density among clusters but also the varying density inside the cluster. image
ImpKmeans: An Improved Version of the KMeans Algorithm, by Determining Optimum Initial Centroids, based on Multivariate Kernel Density Estimation and Kd-Tree
(Budapest Tech, 2024) Senol, Ali
K -means is the best known clustering algorithm, because of its usage simplicity, fast speed and efficiency. However, resultant clusters are influenced by the randomly selected initial centroids. Therefore, many techniques have been implemented to solve the mentioned issue. In this paper, a new version of the k -means clustering algorithm named as ImpKmeans shortly (An Improved Version of K -Means Algorithm by Determining Optimum Initial Centroids Based on Multivariate Kernel Density Estimation and Kd-tree) that uses kernel density estimation, to find the optimum initial centroids, is proposed. Kernel density estimation is used, because it is a nonparametric distribution estimation method, that can identify density regions. To understand the efficiency of the ImpKmeans, we compared it with some state-of-the-art algorithms. According to the experimental studies, the proposed algorithm was better than the compared versions of k -means. While ImpKmeans was the most successful algorithm in 46 tests of 60, the second-best algorithm, was the best on 34 tests. Moreover, experimental results indicated that the ImpKmeans is fast, compared to the selected k -means versions.
