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Öğe ADA-PC: An asynchronous distributed algorithm for minimizing pairwise connectivity in wireless multi-hop networks(Elsevier, 2023) Ugurlu, OnurReliability analysis is of great significance to designing and maintaining wireless multi-hop networks (WMhNs). In WMhNs, several reasons can cause a node to be inoperable, such as hardware failure, software errors, and battery drain. Failure of some critical nodes may partition networks into disconnected segments. The presence of such critical nodes may also reduce the network lifetime since they consume more energy for packet forwarding. Therefore, it is crucial to identify the critical nodes of the networks and strengthen them by adding more nodes surrounding those or creating alternate pathways connecting other nodes to ensure connectivity maintenance in WMhNs. One of the most common approaches in direction is detecting the cut nodes of the networks. However, although finding cut nodes provide helpful information, it may be insufficient for precise reliability analysis since finding cut nodes only does not consider the remaining network. Critical Node Problem (CNP) aims to detect the most important nodes of the network whose removal minimizes the pairwise connectivity (the total number of node pairs connected by at least one path). In other words, the CNP tries to identify a set of nodes whose absence partitions the network into several disconnected segments of similar size. Detecting critical nodes for pairwise connectivity reveals the weak points and bottlenecks of the networks and may help to increase the fault tolerance and lifetime of WMhNs. This paper proposes an Asynchronous Distributed Algorithm for minimizing Pairwise Connectivity (ADA-PC) in WMhNs. To the best of our knowledge, this is the first distributed algorithm for the targeted problem in the network literature. The proposed algorithm uses a distributed Breadth-First Search (BFS) tree which limits bit complexity to O(n.m.log2n) and space complexity to O(d), where d is the network's diameter. The experimental study on both testbed experiment and simulation reveals that the proposed algorithm is capable of finding the most critical nodes with up to 60% lower sent bytes than the existing central algorithms.Öğe Adsorption of thorium (IV) ions using a novel borate-based nano material Ca3Y2B4O12: Application of response surface methodology and Artificial Neural Network(Pergamon-Elsevier Science Ltd, 2023) Kaynar, Umit H.; Kaptanoglu, I. . Gozde; Cam-Kaynar, Sermin; Ugurlu, Onur; Yusan, Sabriye; Aytas, Sule; Madkhli, A. Y.Since nuclear wastes are the most important wastes in terms of health and the environment, they are evaluated differently within nuclear reactors as well as in terms of their use in medical and industrial applications. In some cases, emergency intervention is necessary due to the amount of radioactivity or the physical and/or chemical conditions. . The purpose of this study is to investigate the adsorption properties of nano Ca3Y2B4O12 (CYBO) material synthesized by the sol-gel combustion method for the adsorption of Thorium (IV) from an aqueous medium. We tested how pH (3???8), the concentration of Th (IV) (25???125 mg/L), amount of adsorbent value (0.005???0.08 g) and temperature (20???60 ???C), affect adsorption efficiency. The best possible combinations of these parameters were examined by Response Surface Methodology (RSM) and Artificial Neural Network (ANN). R2 values for RSM and ANN were 0.9964 and 0.9666, respectively. According to the models, the adsorption capacity under the optimum conditions determined for the RSM and ANN model was found to be 134.62 mg/g and 125.12 mg/g, respectively.Öğe Comparative analysis of centrality measures for identifying critical nodes in complex networks(Elsevier, 2022) Ugurlu, OnurOne of the fundamental tasks in complex networks is detecting critical nodes whose removal significantly disrupts network connectivity. Identifying critical nodes can help analyze the topological characteristics of the network, such as vulnerability and robustness. This work considers a well-known critical node detection problem variant, Maximize the Number of Connected Components Problem, which aims to find a set of nodes whose removal maximizes the number of connected components and compares the centrality measures for detecting these nodes. While the existing literature focused only on small datasets, this work analyzes the widely used topology-based centrality measures on several synthetic and real-world networks. Our findings show that degree-like centralities are more relevant measures than path-like centralities for disconnecting networks into several connected components. However, our results also indicate that the traditional centrality measures cannot detect the most vital critical nodes. To overcome this drawback, a new centrality measure, namely Isolating Centrality, that aims to identify the nodes that significantly impact network connectedness is presented. The comprehensive computational study demonstrates that the proposed measure outperforms traditional measures in identifying critical nodes.Öğe Detecting the Most Vital Articulation Points in Wireless Multi-Hop Networks(Ieee-Inst Electrical Electronics Engineers Inc, 2023) Akram, Vahid Khalilpour; Ugurlu, OnurAn articulation point is a node whose removal partitions the network into disconnected segments. The articulation points may affect the reliability and efficiency of wireless multi hop networks from different aspects. Although all articulation points destroy the connectivity of the network, their negative impact on the network is not equal. Removing some articulation points may disconnect a large subset of nodes or generate a large number of partitions, while removing some other articulation points may only disconnect a few nodes. In this paper, we present two novel problems for identifying the most vital articulation points that significantly impact the network. The first problem is finding the p most important articulation points that minimize the largest connected component in the remaining network. The second problem is finding the p most important articulation points whose removal maximizes the number of partitions in the network. We prove that both problems are NP-Hard and propose a distributed algorithm to identify the vital articulation points in both problems. The proposed algorithm establishes a distributed depth-first search tree to identify the articulation points, assigns a score to each articulation point, and selects the prominent articulation points based on their scores. We compare the proposed algorithm with a brute force-based exact algorithm. The simulation result shows that after removing the detected prominent articulation points by the proposed algorithm, the maximum difference between the largest partition size and the number of partitions with the optimal solutions are less than 27.6% and 28.2%, respectively, while the sent bytes of the proposed algorithm can be 89.9% lower.Öğe The effect of well-known burn-related features on machine learning algorithms in burn patients' mortality prediction(Turkish Assoc Trauma Emergency Surgery, 2023) Yazici, Hilmi; Ugurlu, Onur; Aygul, Yesim; Yildirim, Mehmet; Ucar, Ahmet DenizBACKGROUND: Burns is one of the most common traumas worldwide. Severely injured burn patients have an increased risk for mortality and morbidity. This study aimed to evaluate well-known risk factors for burn mortality and comparison of six machine learning (ML) Algorithms' predictive performances. METHODS: The medical records of patients who had burn injuries treated at Izmir Bozyaka Training and Research Hospital's Burn Treatment Center were examined retrospectively. Patients' demographics such as age and gender, total burned surface area (TBSA), Inhalation injury (II), full-thickness burns (FTBSA), and burn types (BT) were recorded and used as input features in ML models. Patients were analyzed under two groups: Survivors and Non-Survivors. Six ML algorithms, including k-Nearest Neighbor, Decision Tree, Random Forest, Support Vector Machine, Multi-Layer Perceptron, and AdaBoost (AB), were used for predicting mortality. Several different input feature combinations were evaluated for each algorithm. RESULTS: The number of eligible patients was 363. All six parameters (TBSA, Gender, FTBSA, II, Age, BT) that were included in ML algorithms showed a significant difference (p<0.001). The results show that AB algorithm using all input features had the best prediction performance with an accuracy of 90% and an area under the curve of 92%. CONCLUSION: ML algorithms showed strong predictive performance in burn mortality. The development of an ML algorithm with the right input features could be useful in the clinical practice. Further investigations are needed on this topic.Öğe Gradual Loss of Social Group Support during Competition Activates Anterior TPJ and Insula but Deactivates Default Mode Network(Mdpi, 2023) Ozkul, Burcu; Candemir, Cemre; Oguz, Kaya; Eroglu-Koc, Seda; Kizilates-Evin, Gozde; Ugurlu, Onur; Erdogan, YigitGroup forming behaviors are common in many species to overcome environmental challenges. In humans, bonding, trust, group norms, and a shared past increase consolidation of social groups. Being a part of a social group increases resilience to mental stress; conversely, its loss increases vulnerability to depression. However, our knowledge on how social group support affects brain functions is limited. This study observed that default mode network (DMN) activity reduced with the loss of social group support from real-life friends in a challenging social competition. The loss of support induced anterior temporoparietal activity followed by anterior insula and the dorsal attentional network activity. Being a part of a social group and having support provides an environment for high cognitive functioning of the DMN, while the loss of group support acts as a threat signal and activates the anterior temporoparietal junction (TPJ) and insula regions of salience and attentional networks for individual survival.Öğe A Greedy Algorithm for Minimum Cut into Bounded Sets Problem(IEEE, 2021) Ugurlu, Onur; Akram, Vahid Khalilpour; Eliiyi, Deniz TurselFinding critical links and weak points is an important task in almost all types of networks. Minimum cuts provide useful information about the critical links. However, finding a minimum cut of a network may provide insufficient or misleading information on critical links since the number of disconnected nodes in the residual network is not taken into account in this problem. In this work, we study the minimum cut into bounded sets problem, which limits the number of nodes in portioned sets. Finding the minimum cut into bounded sets can provide useful information on important critical links in a different network, whose failure has a hard and unacceptable effect. The minimum cut into bounded sets problem is an open NP-Complete problem. We propose a greedy algorithm for this problem with O(c x n(2)) time complexity and present computational results on random networks. To the best of our knowledge, the proposed algorithm is the first heuristic for the minimum cut into bounded sets problem.Öğe A localized distributed algorithm for vertex cover problem(Elsevier, 2022) Akram, Vahid Khalilpour; Ugurlu, OnurFinding the minimum vertex cover of a given graph is a well-known NP-Hard problem that has many applications in various fields. In this paper, we propose a distributed localized algorithm for detecting vertex cover using 2-hop local neighborhood information in the distributed systems. We propose a scoring based policy and add the 1-hop neighbors of nodes with the highest score among their 2-hop neighbors to the vertex cover. The score of nodes is calculated by dividing the number of uncovered edges in their local subgraph by the number of their 1-hop neighbors. In this way, the score of each node determines the average coverage ratio by each neighbor of that node. The time and bit complexities of the proposed algorithm are O(n/Delta) and O(n(2) x log n) respectively, where Delta is the maximum node degree and n is the number of nodes. The comprehensive simulation results showed that the proposed algorithm could find up to 11% smaller solutions than the existing distributed algorithms with less than 1% difference of optimum solutions in most of the evaluated graphs.Öğe Minimization of the threshold voltage parameter of the co-doped ZnO doped liquid crystals by machine learning algorithms(Nature Portfolio, 2023) Onsal, Guelnur; Ugurlu, Onur; Kaynar, Umit H.; Eliiyi, Deniz TurselThis study aims to examine the influence of the co-doped semiconductor nanostructure (Al-Cu):ZnO on the electro-optical properties of the E7 coded pure nematic liquid crystal structures and minimize the threshold voltage of pure E7 liquid crystal. To determine the ideal concentration ratios of the materials for the minimum threshold voltage, we employed different machine learning algorithms. In this context, we first produced twelve composite structures through lab experimentation with different concentrations and created an experimental dataset for the machine learning algorithms. Next, the ideal concentration ratios were estimated using the AdaBoost algorithm, which has an R-2 of 96% on the experimental dataset. Finally, additional composite structures having the estimated concentration ratios were produced. The results show that, with the help of the employed machine learning algorithms, the threshold voltage of pure E7 liquid crystal was reduced by 19% via the (Al-Cu):ZnO doping.
