Sürdürülebilir Kalkınma Amaçları
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- MakaleAçık Erişim2025Scopus SJRQ2Advanced synthesis and comprehensive characterization of nanoparticle-reinforced epoxy-based biocomposites derived from modified palm oil
Şahal, Hakan; Aydoğmuş, Ercan; Canpolat, Erdal; Kaya, Mehmet
In this research, new biocomposites were synthesized by two-step modification of palm oil (MPO). The raw material (MPOSA) was epoxidized in the first step and obtained by reacting with 4,4-diaminodiphenyl sulfone in the second step. Silicon carbide (SiC), MPOSA, bisphenol A diglycidyl ether, and epoxy resin were used to obtain the biocomposites. After this process, necessary physical tests and chemical analyses were performed. Fourier transform infrared spectroscopy (FTIR) and scanning electron microscope (SEM) analyses were used to characterize the biocomposites. The research included measurements of bulk density, Shore D hardness, thermal conductivity, and thermal stability of the biocomposite. The results indicated that MPOSA reinforcement decreased the bulk density of the biocomposite in proportion to the doping level, and Shore D hardness tests revealed a decrease in hardness with the addition of MPOSA. The increase in MPOSA reinforcement also led to a reduction in the thermal conductivity coefficient. In experimental studies, the optimum reinforcement ratio was determined as 1 wt.% nano-SiC, 10 wt.% glass fiber, and 10 wt.% MPOSA. Excessive MPOSA reinforcement adversely affected the matrix structure and pore distribution of the nanocomposite due to aggregation due to increased intermolecular interaction. Thermal decomposition results showed that MPOSA reinforcement reduced the activation energy of the nanocomposite. This study successfully developed an economical, environmentally friendly, and easily processed biocomposite and contributed to the development of functional and sustainable alternatives.
AtıfScopus3WoS3OpenAlex3
İncele - MakaleAçık Erişim2024Scopus SJRQ2New sulfa drug derivatives and their zinc(II) complexes: synthesis, spectroscopic properties and in vitro cytotoxic activities
Şahal, Hakan; Öz, Samet; Keskin, Tuba; Tekin, Suat; Canpolat, Erdal; Kaya, Mehmet
Synthesis of four sulfa drug derivatives (L1–L4) and Zn(II) complexes derived from sulfonamide group antibiotic substances was carried out using the hydrothermal technique (HT) and their structures of the obtained compounds were explained using elemental analysis (EA), FT-IR and NMR (1H- and 13C-). Cytotoxic activities of four novel sulfa drug based-Schiff base compounds and their Zn(II) complexes were determined by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) assay using MCF-7 (human breast cancer), Caco-2 (human colorectal adenocarcinoma), A2780 (human ovarian cancer) and LNCaP (human prostate adenocarcinoma) cell lines. LogIC50 values of all obtained compounds were computed with the Graphpad Prism 6 program after 24 h of treatment for MCF-7, Caco-2, A2780 and LNCaP cells. Comet assay experiments were performed using LogIC50 concentrations of all compounds to determine DNA damage. Based on the data obtained, all compounds significantly decreased MCF-7, Caco-2, A2780 and LNCaP cell viability compared to the control groups (p < 0.05).
AtıfScopus6OpenAlex5
İncele - BildiriAçık Erişim2019Predicting Links in Complex Bipartite Networks Based on Strengthening Projections
Aslan, Serpil; Kaya, Mehmet
Link prediction in complex networks is a topic of high interest for many scientists that studied different areas. Many real-world complex networks have naturally bipartite structured. In this work, we proposed a link prediction method for the complex bipartite network. Recently, while link prediction in complex bipartite networks has been extensively studied, there isn't still sufficiently suitable methods for this networks. The previous works usually have some limitations. They first map the bipartite network into the unimodal projection networks. Then, they perform the link prediction using only the properties of the projection networks. However, these projection networks involve many node pairs with weak relationships when used in networks constructed from real-world data. These weak relationships lead to low probability predictions and high computation time for the algorithm. In this paper, to overcome these limitations, we first map the bipartite network into strengthening projection network model that consists of nodes that have only strong relationships. Based on this projection network, we presented the link prediction method for the complex bipartite network. We conducted extensive experiments on complex bipartite networks. The results of the experiments demonstrate that our proposed method can effectively solve the link prediction problem in complex bipartite networks.
AtıfScopus2OpenAlex2İncele - BildiriAçık Erişim2019A Hybrid recommendation system in co-Authorship networks
Aslan, Serpil; Kaya, Mehmet
Recommender systems are an important field that aim to direct users more quickly to their needs. Scientific research studies in academic field are mostly carried out by collaboration between scientists. Finding new collaborations and analyzing the quality of them are quite complicated situations. Therefore, developing a system that recommend strong collaborations for scientists can be an invaluable tool. In this study, we present a hybrid recommendation method that uses both collaborative filtering and a network-based method by examining the structure of the co-Authorship network that was created by using a real database of scientific articles in computer science. In the constructed network, the nodes represent scientists and links represent the coauthorship relationships. If two scientists in the network have been co-Author of at least one article, they are considered to be connected. Many of the present recommendation methods in this area take into account only the common neighbors of the nodes, while calculating the similarity of the node pairs in the network. To overcome these limited methods, we use the local community-based similarity indexes which also take into account the similarities between the common neighbors of the nodes. When the experimental results are examined, they prove the success of the proposed method.
AtıfScopus7WoS3OpenAlex6İncele - MakaleAçık Erişim2019Scopus SJRQ2Predicting potential links by using strengthened projections in evolving bipartite networks
Aslan, Serpil; Kaya, Buket; Kaya, Mehmet
The link prediction problem can be used for predicting the link changes that are difficult to understand and costly in network science. So far, many different models have been proposed to predict links in the network. Most of the proposed models in the link prediction have focused on the unimodal networks. In this paper, we present a link prediction model that attempts to predict links in the large-scale bipartite networks. The proposed method consists of two stages: The first stage is the extraction of the potential link set. A limitation of the previous works that addressed the link prediction problem in bipartite networks is that only the structural properties of the projected networks that were transformed from the bipartite networks are used to extract the potential links. In this paper, in addition to the structural properties of the projected networks, we take into account the structural properties of the bipartite network. Also, we employ the strengthened projection network model instead of the classical projection network models. The second stage is the calculation of prediction score of each potential link. The classical proximity measures have several disadvantages in dynamic complex networks. When network evolution that considers valuable information between node pairs are ignored, the potential information source is not efficiently used in the link prediction. Therefore, in this stage, we propose a time-aware proximity measure based on social network evolution, instead of classical proximity measures using the current state of the network. To the best of our knowledge, this is the first effort that considers the evolution in bipartite networks. To test the performance of the proposed method, we made some experiments on two real networks that have different frame size. Experimental results show that the success of our method is promising.
AtıfScopus14WoS5OpenAlex9İncele - MakaleAçık Erişim2018Scopus SJRQ1Topic recommendation for authors as a link prediction problem
Aslan, Serpil; Kaya, Mehmet
Recently, link prediction studies on large-scale and complex networks have particularly become the focus of interest for researchers in various scientific fields. Many complex networks created from the real-world data contain bipartite structure by nature. Bipartite networks are a kind of complex networks that represent the interactions between the different node groups. Almost all of the previous studies on link prediction in bipartite networks focus on using the properties of projection networks to predict the relations between the node pairs. In this study, a novel similarity-based link prediction method based on strengthening weighted projection is proposed to predict the potential links between the authors and the topics in the large-scale bipartite academic information network created from the real-world data. Since information loss occurs when bipartite networks are converted to unimodal networks, it should be noted that when making a link prediction in this paper, both the bipartite network and the information on strengthening unimodal network obtained from the bipartite network are used. To evaluate the proposed method, a bipartite network was first created from a real dataset consisting of authors and their work. Then the method was tested on this network. Experimental results demonstrate that it is possible to obtain faster and more accurate link prediction results by the proposed method.
AtıfScopus35WoS25OpenAlex34İncele - BildiriYalnız künye2017Link Prediction Methods in Bipartite Networks
Aslan, Serpil; Kaya, Mehmet; Adali, E
Nowadays, social networks, whose application areas are increasing day by day; Such as data mining, protein-gene interaction networks, online social networks, transport networks, academic information networks, and telecommunications networks. Many real-world complex networks have a naturel bipartite structure. In bipartite networks, which have become the focus of many researchers who have been dealing with social network analysis in recent years, connection estimation methods are the basic methods used to understand the structure of these networks. This study is a survey work that describes the connection in a bipartite network that is suitable for the nature of today's huge data networks. In this study, a bipartite network interface is an introductory guide for researchers who plan to work on this subject by examining the internal links and bipartite network projection methods.
AtıfWoS4İncele - BildiriYalnız künye2016Predicting Links in Weighted Disease Networks
Gül, Serpil; Kaya, Mehmet; Kaya, Buket
This paper proposes a link prediction method to estimate what sort of disease risks may have been carried by the patients via surveying their and similar patients' medical histories. It is possible to make future oriented predictions with link prediction by deriving new data within a network structure. It is attempted to estimate the future structure of the network and the relations which will be created or abandoned by the individuals on the basis of the nodes within the network structure and the relations between these nodes. For this purpose, an undirected weighted disease network is first created by using the data of patients which were subjected to Hemogram test at F. rat University Hospital during 2013. In the disease network, each node represents a disease and the links represent a relationship between diseases. By the developed method, then disease risk prediction is made through a proactive approach by determining what sorts of disease risks were carried by the patients applying with certain complaints. Experimental results demonstrate the applicability of the link prediction in these networks.
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