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Analysis and classification of the mobile molecular communication systems with deep learning
(Springer Heidelberg, 2022) Isik, Ibrahim; Er, Mehmet Bilal; Isik, Esme
Nano networks focused on communication between nano-sized devices (nanomachines) is a new communication concept which is known as molecular communication system (MCs) in literature. The researchers have generally used fixed transmitter and receiver for MCs models to analyze the fraction of received molecules and signal to interference rate etc. In this study, contrary to the literature, a mobile MC model has been used in a diffusion environment by using five bits. It is concluded that when the receiver and transmitter are mobile, distance between them changes and finally this affects the probability of the received molecules at the receiver. After the fraction of received molecules is obtained for different mobility values of Rx and Tx (Drx and Dtx), deep learning's bi-directional long short-term memory (Bi-LSTM) model is applied for the classification of Rx and Tx mobilities to find the best MC model with respect to fraction of received molecules. Finally it is obtained that when the mobilities of Rx and Tx increase, the fraction of received molecules also increases. Bi-LSTM model of Deep learning is used on a data set consisting of five classes. The suggested model's accuracy, precision, and sensitivity values are obtained as 98.05, 96.49, and 98.01 percent, respectively.
Artificial neural network modeling for the effect of fly ash fineness on compressive strength
(2021) Demir Şahin, Demet; Isik, Esme; Isik, Ibrahim; Cullu, Mustafa
In the literature, there has been a lot of discussion over the compressive strength of concrete made with high-calcium fly ash instead of cement. However, no research has been done to determine the influence of ground fly ash on compressive strength using experimental and artificial neural network (ANN) models when they are used in place of cement at various replacement ratios. The size and replacement ratio of fly ash give maximum durability of the concrete when utilized in the cement composition, according to the ANN model employed in this study, allowing to forecast the result without using a high-cost and energy-intensive operation like grinding. In this study, short and long-term compressive strength of concrete of class C fly ash is analyzed at six different Blaine fineness values (1834, 1852, 1930, 1992, 1995, and 2018 cm2/g) and three different fly ash substitution rates (10, 30, and 50%) for 270 supplemental concrete samples. In addition, based on the experimental results, an ANN model was proposed to simulate and predict the compressive strength of concrete. In this proposed model, substitution rate, Blaine fineness, and curing time (day) were used as input to simulate the value of compressive strength for 3, 7, 28, 56, and 90 curing times with 99% accuracy. Also, the value of compressive strength was predicted for 120 curing days. The predicted target values were compared with the experiment resulted in a better correlation coefficient of 0.99. Thus, the results attained from this ANN model were found to be effective in predicting the relationship between fly ash fineness and compressive strength at any given operating condition.
Assessing the Impact of Multimodal Transportation on Economic Growth: A Machine Learning and Cointegration Approach in 28 Countries
(Tu Delft Open Publishing, 2025) Isik, Esme; Ozyilmaz, Ayfer; Bayraktar, Yuksel; Toprak, Metin; Olgun, Mehmet Firat; Senturk, Nazli Keyifli
In this study, the effect of freight and passenger transport in different modes on economic growth is determined for 28 selected countries. The Westerlund cointegration test is used to reveal the long-term relationship between freight and passenger transportation and growth. According to the cointegration analysis, all transportation modes (road, rail, and air) are cointegrated with growth. Additionally, machine learning models were used to predict growth based on each transportation mode for each country for the upcoming four years and to determine the importance of the input parameters. According to the importance of the parameter analysis, for the entire panel, rail transport is the most effective transport mode for economic growth. On a country-by-country basis, the findings differ. Rail transport is the strongest transport mode for growth in high-income countries. However, although it is not the dominant mode, the relative impact of air passenger transport is strong. In upper middle-income countries, there generally is not a dominant mode of transport, but in general, freight transport is important to economic growth. In passenger transportation, air passenger transport is the most prominent mode in these countries. In lower middle-income countries, rail freight is the strongest transport mode for economic growth.
Bacterial Chemotaxis in Molecular Communication: Experimental and Simulation Analysis of Receiver Placement and Gradient Dynamics
(Ieee-Inst Electrical Electronics Engineers Inc, 2026) Duman, Mustafa Ozan; Isik, Ibrahim; Isik, Esme
Bacteria-based nanonetworks (BNs) represent a promising strategy for nanoscale information transfer, utilizing bacterial motility and chemotaxis for targeted message delivery. This study analyzes BN performance through both experimental validation and a custom-developed three-dimensional (3D) simulation program built in MATLAB, focusing on receiver (RX) placement, chemoattractant release rate (Q), and bacterial lifespan. The simulation employs experimentally validated parameters and models bacterial behavior under various spatial configurations. Results demonstrate that RX positioning significantly affects communication efficiency, with asymmetric placement causing uneven chemoattractant gradients and reduced success rates. While higher Q values improve reach time and delivery success, bacterial lifespan becomes a limiting factor at extended distances. Experimental findings using agar-based assays confirm a threshold distance beyond which bacterial motility becomes ineffective. These insights provide practical guidance for optimizing BN systems by balancing signal strength with biological constraints. Future work should explore adaptive bacterial strategies and dynamic environmental conditions to further enhance BN reliability and applicability in areas such as targeted drug delivery and biosensing.
Chemotaxis-Driven Molecular Communication in Nanonetworks: Simulating E. coli Behavior and Performance
(Institute of Electrical and Electronics Engineers Inc., 2025) Duman, Mustafa Ozan; Isik, Ibrahim; Bilaler, Mehmet; Tagluk, Mehmet Emin; Isik, Esme
Bacteria-based nanonetworks (BN) show significant potential for revolutionizing nanoscale communication, particularly in fields like medicine and environmental monitoring. This study models the chemotaxis of Escherichia coli (E. coli) in a 2D environment using a customdeveloped MATLAB simulation to understand communication effectiveness. We investigate the impact of chemoattractant release rate (Q), transmitter-receiver distance (d), and bacterial lifespan. Key findings reveal a trade-off between communication range and energy consumption: while higher Q values extend range, they also increase resource usage. A Q value of 10-14 ~mol / s is identified as providing a balanced approach. Furthermore, simulations highlight that bacterial lifespan inherently limits longer communication range, suggesting the potential for nanomachine relays in future BN designs. Future research will expand these models to incorporate 3D environments and multi-bacterium interactions, enhancing their applicability for real-world scenarios. © 2025 IEEE.
Classification of Diffusion Constants of Transmitter and Receiver and Distance Between Them Using Mobile Molecular Communication via Diffusion Model
(Springer Heidelberg, 2025) Er, Mehmet Bilal; Isik, Ibrahim; Kuran, Umut; Isik, Esme
Molecular communication (MC) holds promise for enabling communication in scenarios where traditional wireless methods may be impractical or ineffective, offering unique capabilities for a range of applications in both natural and engineered systems. In this research, a novel approach to MC is explored, diverging from the standard use of stationary transmitter and receiver models typically found in the field. The study introduces a dynamic MC model, where both the transmitter and receiver are mobile within a diffusion environment. This model operates using a 5-bit system. The key finding is that the mobility of these nanodevices alters their distance, which in turn impacts the likelihood of molecule reception at the receiver. The study employs deep learning techniques, specifically a combination of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, to categorize the mobility patterns of the receiver (Rx) and transmitter (Tx). By analyzing various mobility rates (Drx and Dtx) and distances between the Tx and Rx, the research successfully identifies the most efficient mobile MC model in terms of molecule reception rates. The use of Linear Support Vector Machine alongside the CNN and LSTM hybrid feature vector resulted in an 87.68% accuracy in predicting diffusion coefficients. Moreover, using a Cubic Support Vector with the same hybrid feature vector, the study achieved an 88.09% accuracy in estimating the distance between the transmitter and receiver. The study concludes that an increase in the mobilities of Rx and Tx correlates with a higher rate of molecule reception.
Difüzyon yolu ile moleküler haberleşme modelinin birikimli dağılım fonksiyonları ile analizi
(2024) isik, ibrahim; Isik, Esme; ATEŞ, Abdullah
Nano boyutlu cihazlar (nano makineler) arasında yeni bir iletişim yöntemi olan Moleküler Haberleşme (MOH), son donemde literatürde artarak ilgi görmektedir. Alıcıya ulaşan moleküllerin sayısı ve molekül girişim oranı gibi faktörleri analiz etmek için çok sayıda MOH modeli kullanılmıştır. Bununla birlikte, mevcut MOH modellerinde gözlemlenen ortak bir eğilim, taşıyıcı moleküllerin difüzyon ortamı içindeki hareketini açıklamak için Normal dağılım fonksiyonunun baskın olarak kullanılmasıdır. Mevcut literatürün aksine, bu çalışma optimum performansa sahip MOH modelini belirlemek için alınan molekül sayısını dikkate alarak moleküllerin difüzyon ortamındaki hareketi için alternatif dağılım fonksiyonlarını kapsamlı bir şekilde araştırmayı amaçlamaktadır. Çalışma, literatürde kapsamlı bir şekilde araştırılan sistem ve çevresel parametrelerin iyileştirilmesine odaklanarak MOH sisteminin performansının önemli ölçüde artırılabileceğini öngörmektedir. Sonuç olarak, bu araştırma mevcut bilgi birikimine değerli iç görüler katmaya çalışmaktadır. Bu çalışmada, uç değer dağılımı (EVRND), normal dağılım (NRND), t-dağılım (TRND), genelleştirilmiş uç değer dağılım (GEVRND) ve genelleştirilmiş Pareto (GPRND) rastgele dağılım fonksiyonları, haberleşme sisteminin performansını önemli ölçüde etkileyen farklı sistem parametreleri ile karşılaştırılarak en iyi MOH modeli bulunmaya çalışılmıştır. Analizler, GPRND dağılımının en yüksek performansı, NRND dağılımının ise en kötü performansı gösterdiğini ortaya koymuştur. Literatürdeki MOH modellerinin analizinde NRND dağılımının yaygın kullanımı göz önüne alındığında, bu çalışmanın önemi bir kez daha ortaya çıkmaktadır.
Enhancing high sensitive hydrogen detection of Bi2O3 nanoparticle decorated TiO2 nanotubes
(Elsevier Science Sa, 2024) Isik, Esme; Tasyurek, Lutfi Bilal; Tosun, Emir; Kilinc, Necmettin
An electrochemical anodization technique was used to create a hydrogen gas sensor based on TiO2 nanotubes decorated with bismuth oxide (Bi2O3). Bismuth nitrate pentahydrate (Bi(NO3)3 center dot 5H2O) was employed as the source material for Bi2O3. The resulting nanotubes were annealed at 500 degrees C, revealing an amorphous structure with a mixed phase of rutile and anatase. Platinum (Pt) electrodes, with a thickness of 100 nm, were coated onto the Bi2O3@TiO2/Ti and TiO2/Ti structures for sensor testing. Energy dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and field emission scanning electron microscopy (FESEM) were used to examine the structural, morphological, and surface properties of the Bi2O3@TiO2 and TiO2 nanotubes. The hydrogen sensing properties of the Pt/Bi2O3@TiO2/Ti and Pt/TiO2/Ti devices were evaluated at room temperature, with hydrogen concentrations ranging from 1000 ppm to 10 %. The I-V characterization of the sensor devices under 1 % H2 exhibited typical Schottky-type behavior. Remarkably, the Pt/Bi2O3@TiO2/Ti structure demonstrated a sensor response 1 x 107 times higher than that of in a dry air environment when the same voltage was applied under up to 1 % H2 conditions. The uniform dispersion of Bi2O3 nanoparticles throughout the structure contributed to the enhanced sensor response in the presence of H2.
Enhancing the performance of TiO2 nanotube-based hydrogen sensors through crystal structure and metal electrode
(Pergamon-Elsevier Science Ltd, 2024) Tasyurek, Lutfi Bilal; Isik, Esme; Isik, Ibrahim; Kilinc, Necmettin
In this research, the effect of metal electrodes and crystalline phase on gas detection of titanium dioxide (TiO2) nanotube-based hydrogen (H2) sensors was investigated. TiO2 nanotubes were produced using glycerol-based electrolyte and annealed at 300 degrees C and 700 degrees C to change the anatase and rutile crystalline phases, respectively. TiO2 nanotubes were coated by platinum (Pt), palladium (Pd), gold (Au) and silver (Ag) electrodes to fabricate metal/TiO2 nanotubes Ti H2 sensor devices and then the current-voltage (I-V) characteristics were investigated at room temperature. The structural properties of TiO2 nanotubes were characterized by SEM, FE-SEM, XRD, and Raman techniques. The H2 detection properties of the sensors were examined at the 1000 ppm - 5% H2 concentration range. The crystal structure and metal electrodes are the main factors that affect the H2 sensing properties of TiO2 nanotube-based sensors. The effect of crystal forms on sensitivity was not the same as for metal electrodes. The underlying sensing mechanisms for different types of metal electrodes and crystal structures are discussed and the relevance of their sensing performance to nanotubes and electronic properties is investigated. In addition, discussion of each metal electrode and crystal structure will make important contributions to the development of H2 sensors. The Pd-coated device annealed at 700 degrees C showed the best detection performance.(c) 2023 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
Optimization of distribution function and model parameters for molecular communication via diffusion with OtoO approximation
(Elsevier, 2024) Akpamukcu, Mehmet; Ates, Abdullah; Isik, Ibrahim; Isik, Esme
The analysis is generally conducted in stationary receiver and transmitter models in a diffusion environment for the fundamental Molecular communication (MOC) models. However, a mobile MOC model is employed in this study, deviating from the existing literature. This mobile MOC model considers the mobility of all variables in the diffusion environment, including the transmitter, receiver, and molecules. Firstly, a novel MOC model is proposed, departing from the conventional normal distribution for the mobility of variables. Instead, alternative distribution functions such as the Pareto distribution, extreme value distribution, t-distribution, and generalized extreme value distribution are employed. Furthermore, the system's performance is enhanced by optimizing the distribution function and model parameters, such as the diffusion coefficient, using the optimization of optimization (OtoO) approach. In this approach, the Multi-Verse Optimization (MVO) algorithm serves as the primary algorithm, while the Grey Wolf Optimization (GWO) algorithm functions as the auxiliary algorithm. Essentially, the MVO algorithm optimizes the parameters of the MOC model, while simultaneously, the GWO algorithm optimizes the impact of the optimization processes of MVO on the parameters ``p and ``N as well as the constant parameter of the distribution function. By optimizing both the parameters of the MOC model and the distribution function, the number of received molecules is significantly increased. Therefore, this study not only improves the results of the MOC model structure based on different distribution functions but also optimizes all parameters of the proposed model using the MVO-GWO OtoO approach.
Pandemics, Income Inequality, and Refugees: The Case of COVID-19
(Routledge Journals, Taylor & Francis Ltd, 2024) Buyukakin, Figen; Ozyilmaz, Ayfer; Isik, Esme; Bayraktar, Yuksel; Olgun, Mehmet Firat; Toprak, Metin
Refugees are more vulnerable to COVID-19 due to factors such as low standard of living, accommodation in crowded households, difficulty in receiving health care due to high treatment costs in some countries, and inability to access public health and social services. The increasing income inequalities, anxiety about providing minimum living conditions, and fear of being unemployed compel refugees to continue their jobs, and this affects the number of cases and case-related deaths. The aim of the study is to analyze the impact of refugees and income inequality on COVID-19 cases and deaths in 95 countries for the year 2021 using Poisson regression, Negative Binomial Regression, and Machine Learning methods. According to the estimation results, refugees and income inequalities increase both COVID-19 cases and deaths. On the other hand, the impact of income inequality on COVID-19 cases and deaths is stronger than on refugees.
Predictive Modeling of Bacteria-Based Nanonetwork Performance Using Simulation-Driven Machine Learning and Genetic Algorithm Optimization
(Wiley-V C H Verlag Gmbh, 2026) Duman, Mustafa Ozan; Isik, Ibrahim; Er, Mehmet Bilal; Tagluk, Mehmet Emin; Isik, Esme
Bacteria-based nanonetwork (BN) offers a biologically inspired solution for enabling information exchange between nanomachines (NMs) in environments where traditional communication methods are ineffective. This study presents a 2D simulation model of a BN system that captures the chemotactic behavior of a single Escherichia coli (E. coli) bacterium navigating from a transmitter (TX) toward a receiver (RX) under varying environmental conditions. Key parameters, which are chemoattractant release rate (Q), TX-RX distance (d), and bacterial lifespan (), are systematically varied to evaluate their impact on communication performance, measured in terms of reach time and success rate. To enable accurate performance prediction without the need for computationally expensive repeated simulations, an analytical model is constructed using various machine learning (ML) techniques, including Linear Regression (LR), Random Forest (RF), and Multi-Layer Perceptron (MLP). Hyperparameters of MLP are optimized using a Genetic Algorithm (GA), significantly enhancing predictive accuracy and training stability. The results demonstrate the effectiveness of integrating dynamic simulation with data-driven modeling and hyperparameter optimization to represent complex system behavior. This framework offers valuable design insights for BN system development and supports the creation of efficient, scalable nanonetworks.
THE EFFECT OF CRYSTAL STRUCTURE AND METAL ELECTRODES ON GAS DETECTION IN TiO2 NANOTUBES H2 SENSORS
(International Association for Hydrogen Energy, IAHE, 2022) Tasyurek, Lutfi Bilal; Isik, Esme; Isik, Ibrahim; Kilinc, Necmettin
In this study, the effects of various metal electrodes and various crystal structures on hydrogen (H2) gas sensors based on titanium dioxide (TiO2) nanotubes were investigated. For the production of TiO2 nanotubes, anodization method consisting of an electrolyte containing 0.5wt% NH4F in 85% pure glycerol solution was applied. Scanning electron microscope (SEM) images of the obtained TiO2 nanotubes were examined. In order to see the effect of different crystal structures of TiO2, amorphous, anatase and rutile phases were obtained by annealing of samples. Each of the Ti/TiO2 nanotubes/metal (Pd, Pt, Au, and Ag) gas sensors, obtained by coating with palladium (Pd), platinum (Pt), gold (Au) and silver (Ag) electrodes, was tested at room temperature at 1% H2 concentration depending on three different phases. © 2022 Proceedings of WHEC 2022 - 23rd World Hydrogen Energy Conference: Bridging Continents by H2. All rights reserved.
The Impact of Refugees on Income Inequality in Developing Countries by Using Quantile Regression, ANN, Fixed and Random Effect
(Mdpi, 2022) Ozyilmaz, Ayfer; Bayraktar, Yuksel; Isik, Esme; Toprak, Metin; Olgun, Mehmet Firat; Aydin, Serdar; Guloglu, Tuncay
Refugees affect the hosting countries both politically and economically, but the size of impact differs among these societies. While this effect emerges mostly in the form of cultural cohesion, security, and racist discourses in developed societies, it mostly stands out with its economic dimension such as unemployment, growth, and inflation in developing countries. Although different reflections exist in different societies, the reaction is expected to be higher if it affects social welfare negatively. Accordingly, one of the parameters that should be addressed is the effect of refugees on income distribution since the socio-economic impact is multifaceted. In this study, the effect of refugees on income inequality is analyzed by using quantile regression with fixed effects and Driscoll-Kraay Fixed Effect (FE)/Random Effect (RE) methods for the period of 1991 to 2020 in the 25 largest refugee-hosting developing countries. According to the findings of the study, the functional form of the relationship between refugees and income inequality in the countries is N-shaped. Accordingly, refugees first increase income inequality, decrease it after reaching a certain level, and then start increasing it, albeit at a low level.
The Relationship between Health Expenditures and Economic Growth in EU Countries: Empirical Evidence Using Panel Fourier Toda-Yamamoto Causality Test and Regression Models
(Mdpi, 2022) Ozyilmaz, Ayfer; Bayraktar, Yuksel; Isik, Esme; Toprak, Metin; Er, Mehmet Bilal; Besel, Furkan; Collins, Sandra
The aim of this study is to investigate the effect of health expenditures on economic growth in the period 2000-2019 in 27 European Union (EU) countries. First, the causality relationship between the variables was analyzed using the panel Fourier Toda-Yamamoto Causality test. The findings demonstrate a bidirectional causality relationship between health expenditures and economic growth on a panel basis. Secondly, the effects of health expenditures on economic growth were examined using the Random Forest Method for the panel and then for each country. According to the Random Forest Method, health expenditures positively affected economic growth, but on the country basis, the effect was different. Then, government health expenditures, private health expenditures, and out-of-pocket expenditures were used, and these three variables were ranked in order of importance in terms of their effects on growth using the Random Forest Method. Accordingly, government health expenditures were the most important variable for economic growth. Finally, Support Vector Regression, Gaussian Process Regression, and Decision Tree Regression models were designed for the simulation of the data used in this study, and the performances of the designed models were analyzed.
The role of institutional quality in the relationship between financial development and economic growth: Emerging markets and middle-income economies
(Elsevier, 2023) Bayraktar, Yuksel; Ozyilmaz, Ayfer; Toprak, Metin; Olgun, Mehmet Firat; Isik, Esme
In this study, the relationship between economic growth and financial development was analyzed for emerging markets and middle-income economies. The effect of financial development on growth, whether there is institutional quality or not, has also been investigated. In addition, which financial development indicator is more effective for growth has been examined. Six institutional quality indicators and seven financial development indicators were used. According to the Dumitrescu-Hurlin causality test results, there is a causality relationship between all financial development indicators and growth. According to the estimation results, financial development indicators have a positive effect on growth in the presence of institutional quality. However, if institutionalization is not included in the model, the effect of financial development indicators on economic growth is statistically insignificant. Copyright (c) 2023 Borsa Istanbul Anonim S,irketi. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Titanium nitride thin films grown by ion beam physical vapor deposition
(Aip Publishing, 2025) Isik, Esme; Sterland, Dominic; Kilinc, Necmettin; Bell, Gavin R.
A novel ion beam physical vapor deposition technique has been developed for TiN thin film deposition. The method uses only standard surface science tools, namely a cold cathode ion gun (normally used for sputter cleaning with Ar) operated with N2 gas and a modified titanium sublimation pump as a Ti PVD source. TiN thin films were deposited onto semi-insulating GaAs (001) substrates, and their physical and electrical properties were measured. X-ray photoelectron spectroscopy suggested predominantly TiN bonding with some oxynitride components. The DC conductivity increased in the range of 200-350 S/cm at temperatures ranging from 300 to 430 K. The behavior was consistent with the correlated barrier hopping model with an activation energy of 0.043 eV. The AC measurements (40 Hz to 0.2 MHz) indicated lower impedance above 10 kHz, possibly from the reduced effect of polarization at grain boundaries and other extended defects. The DC temperature dependence was also maintained even at the highest frequencies. (c) 2025 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license(https://creativecommons.org/licenses/by/4.0/).
Will Outbreaks Increase or Reduce Income Inequality? the Case of COVID-19
(2022) Isik, Esme; OZYILMAZ, AYFER; TOPRAK, METİN; Bayraktar, Yüksel; Büyükakın, Figen; OLGUN, Mehmet Fırat
The effects of economic contractions experienced during pandemic periods on different income sectors and country groups in terms of income inequality are not homogeneous. Due to the fact that COVID-19 has deeply affected the lives of the poor, immigrants, refugees, the homeless, seasonal workers and people with no health insurance, the relationship between the pandemic and income inequality is of great significance . This study aims to find an answer to the question of whether the recent pandemic increased or decreased income inequality. In the study, the effect of COVID-19 on income inequality in 38 countries with different income levels is analyzed with the Artificial Neural Networks (ANN) and Linear Regression (LR) method. In this context, Gini index values for 2020 were estimated using unemployment, inflation and growth data, which are determinants of income distribution, for the periods 2000-2019. According to the analysis findings, while COVID-19 reduces income inequality in some countries, it increases it in others. However, in general, the results of our study show that the overall effect of COVID-19 on income levels in both developed and developing countries has been to increase income inequality.

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