Showing papers by "Zonguldak Karaelmas University published in 2020"

Automatic Detection of Coronavirus Disease (COVID-19) Using X-ray Images and Deep Convolutional Neural Networks

Abstract: The 2019 novel coronavirus disease (COVID-19), with a starting point in China, has spread rapidly among people living in other countries, and is approaching approximately 34,986,502 cases worldwide according to the statistics of European Centre for Disease Prevention and Control. There are a limited number of COVID-19 test kits available in hospitals due to the increasing cases daily. Therefore, it is necessary to implement an automatic detection system as a quick alternative diagnosis option to prevent COVID-19 spreading among people. In this study, five pre-trained convolutional neural network based models (ResNet50, ResNet101, ResNet152, InceptionV3 and Inception-ResNetV2) have been proposed for the detection of coronavirus pneumonia infected patient using chest X-ray radiographs. We have implemented three different binary classifications with four classes (COVID-19, normal (healthy), viral pneumonia and bacterial pneumonia) by using 5-fold cross validation. Considering the performance results obtained, it has seen that the pre-trained ResNet50 model provides the highest classification performance (96.1% accuracy for Dataset-1, 99.5% accuracy for Dataset-2 and 99.7% accuracy for Dataset-3) among other four used models.

Recognition of COVID-19 disease from X-ray images by hybrid model consisting of 2D curvelet transform, chaotic salp swarm algorithm and deep learning technique

Abstract: The novel coronavirus disease 2019 (COVID-19), detected in Wuhan City, Hubei Province, China in late December 2019, is rapidly spreading and affecting all countries in the world Real-time reverse transcription-polymerase chain reaction (RT-PCR) test has been described by the World Health Organization (WHO) as the standard test method for the diagnosis of the disease However, considering that the results of this test are obtained between a few hours and two days, it is very important to apply another diagnostic method as an alternative to this test The fact that RT-PCR test kits are limited in number, the test results are obtained in a long time, and the high probability of healthcare personnel becoming infected with the disease during the test, necessitates the use of other diagnostic methods as an alternative to these test kits In this study, a hybrid model consisting of two-dimensional (2D) curvelet transformation, chaotic salp swarm algorithm (CSSA) and deep learning technique is developed in order to determine the patient infected with coronavirus pneumonia from X-ray images In the proposed model, 2D Curvelet transformation is applied to the images obtained from the patient's chest X-ray radiographs and a feature matrix is formed using the obtained coefficients The coefficients in the feature matrix are optimized with the help of the CSSA and COVID-19 disease is diagnosed by the EfficientNet-B0 model, which is one of the deep learning methods Experimental results show that the proposed hybrid model can diagnose COVID-19 disease with high accuracy from chest X-ray images

An FMEA-based TOPSIS approach under single valued neutrosophic sets for maritime risk evaluation: the case of ship navigation safety

Abstract: As in terrestrial facilities, safety is one of the most important issue in ships. Vessels navigating in many parts of the world face many different, tough and dangerous navigational risks. In this context, twenty-three fundamental risks which are frequently encountered in ship navigation were considered in this study and examined by an FMEA-based TOPSIS approach under single valued neutrosophic sets. Because of the lack of data in the literature, the opinions of the experts (Masters) who have many years of experience in the sector were taken. As a result of the study, extreme weather conditions, injury of crew, loss of input of sensory equipment (depth, gyro, speed etc.), struck by ropes, exposure to high speed machineries under high pressures, loss of maneuverability are very important among these risks. Considering these risks, corrective-preventive action plans and managerial implications for ship navigation have been presented. Consequently, the results of this study have an important warning and solution recommendation regarding ship navigation risks.

Pulmonary rehabilitation principles in SARS-COV-2 infection (COVID-19): A guideline for the acute and subacute rehabilitation

Abstract: Coronavirus disease 2019 (COVID-19) is a contagious infection disease, which may cause respiratory, physical, psychological, and generalized systemic dysfunction. The severity of disease ranges from an asymptomatic infection or mild illness to mild or severe pneumonia with respiratory failure and/or death. COVID-19 dramatically affects the pulmonary system. There is a lack of knowledge about the long-term outcomes of the disease and the possible sequelae and rehabilitation. This clinical practice guideline includes pulmonary rehabilitation (PR) recommendations for adult COVID-19 patients and has been developed in the light of the guidelines on the diagnosis and treatment of COVID-19 provided by the World Health Organization and Republic of Turkey, Ministry of Health, recently published scientific literature, and PR recommendations for COVID-19 regarding basic principles of PR. In this guideline, the contagiousness of COVID-19, recommendations on limited contact of patient with healthcare providers, and the evidence about possible benefits of PR were taken into consideration.

The relationship between obstructive sleep apnea syndrome and obesity: A new perspective on the pathogenesis in terms of organ crosstalk

Abstract: Introduction Obstructive sleep apnea syndrome (OSAS) is a common disorder that has a major impact on public health. The connection between OSAS and obesity is very complex and likely represents an interaction between biological and lifestyle factors. Oxidative stress, inflammation and metabolic dysregulation are both actors involved in the pathogenesis of OSAS and obesity. Also, the current evidence suggests that gut microbiota plays a significant role in the emergence and progression of some metabolic disorders. When the relationship between OSAS and obesity is evaluated extensively, it is understood that they show mutual causality with each other, and that metabolic challenges such as impaired microbiota affect this bidirectional organ interaction, and by ensuing organ injury. Objectives The aim of this study is to investigate the association between OSAS and obesity, and the effect of "organ crosstalk" on the pathogenesis of the relationship and to contribute to the diagnosis and treatment options in the light of current data. Data source We performed an electronic database search including PubMed, EMBASE and Web of Science. We used the following search terms: OSAS, obesity, inflammation, metabolic dysregulation and gut microbiota. Conclusion Obesity and OSAS adversely affect many organs and systems. Besides the factors affecting the diagnosis of the OSAS-obesity relationship, mutual organ interactions among the respiratory system, adipose tissue and intestines should not be ignored for prevention and treatment of OSAS and obesity. Comprehensive clinical trials addressing the efficacy and efficiency of current or potential treatments on therapeutic applications in the OSAS-obesity relationship are needed.

Improved User Fairness in Decode-Forward Relaying Non-Orthogonal Multiple Access Schemes With Imperfect SIC and CSI

Abstract: Non-orthogonal multiple access (NOMA) is one of the key technologies to serve in ultra-dense networks with massive connections which is crucial for Internet of Things. Besides, NOMA provides better spectral efficiency compared to orthogonal multiple access. However, NOMA systems have been mostly investigated only in terms of ergodic capacity (EC) and outage probability (OP) whereas error performances have not been well-studied. In addition, in those analysis, mostly perfect successive interference canceler (SIC) is assumed or the considered imperfect SIC model is not reasonable. Besides, channel state information (CSI) errors are also not considered in most studies. However, this is not the case for the practical scenarios, and these imperfect SIC and CSI effects limit the performance of NOMA involved systems. Moreover, the imperfect SIC causes unfairness between users. In this paper, we introduce reversed decode-forward relaying NOMA (R-DFNOMA) to improve user fairness compared to conventional DFNOMA (C-DFNOMA). In the analysis, we define imperfect SIC effect as dependant to channel fading and with this imperfect SIC and CSI errors, we derive exact expressions of EC and OP. We also provide upper bound for EC, and asymptotic and lower bound expressions for OP. Furthermore, we evaluate bit error performance of the proposed R-DFNOMA and derive exact bit error probability (BEP) in closed-form with imperfect CSI which is the first study analyzing error performances of decode-forward relaying NOMA with imperfect CSI. Then, we define user fairness index in terms of all key performance indicators (KPIs) (i.e., EC, OP and BEP). Based on extensive simulations, all derived expressions are validated, and it is proved that the proposed R-DFNOMA provides better user fairness than C-DFNOMA in terms of all KPIs. Finally, we discuss the effect of power allocations at both source and relay on the performance metrics and user fairness.

The effect of self-directed learning on the relationship between self-leadership and online learning among university students in Turkey

Abstract: More and more people across the world seek a university education. Therefore, universities offer full or partial distance undergraduate and postgraduate degrees to meet that demand. Distance education has become more widespread with advances in the Internet and computer technologies and online learning and teaching software (e.g., Learning Management Systems). The spread of the Coronavirus disease (COVID-19), which broke out in early 2020, has also played a significant role in the increased popularity of distance education. Therefore, investigating and reporting upon university students’ self-leadership (SL) behaviors, self-directed learning (SDL) skills, and online learning (OL) attitudes in such learning environments is both timely and critical. This empirical study used a relational survey model to investigate SL, SDL, and OL among university students. The sample consisted of 835 students in Turkey. Data were collected using the “Self-Directed Learning Scale (SDLS),” “Revised Self-Leadership Questionnaire (RSLQ),” and “Online Learning Attitude Scale (OLAS).” Data were analyzed using descriptive statistics, Pearson coefficient, Confirmatory Factor Analysis (CFA), and Structural Equation Modeling (SEM). SL was moderately and positively correlated with SDL and OL. SDL was moderately and positively correlated with OL. SDL played a fully mediating role in the relationship between SL and OL. SL predicted OL in both the indirect and total effect model. This study addressed university students’ perceptions and tested a model to provide empirical evidence for the relationships and predictions of SL, SDL, and OL. Therefore, it is believed that the results will help advance the constructs of SDL, SL, and OL paradigms. Received: 07 March 2020Accepted: 17 November 2020

A new generalization of Fibonacci hybrid and Lucas hybrid numbers

Abstract: In the paper, we define the q − Fibonacci hybrid numbers and the q − Lucas hybrid numbers, respectively. Then, we give some algebraic properties of q − Fibonacci hybrid numbers and the q − Lucas hybrid numbers.

Adaptive Control of a 3D Printer using Whale Optimization Algorithm for Bio-Printing of Artificial Tissues and Organs

Abstract: Nowadays, 3D printers are frequently used in the field of plastic surgery especially in the bio printing of ear, nose and face-chin prosthesis. In the bio-printing of intertwined complex geometry organs, printing errors varying from model to model are seen. These errors cause the printed organ to not function properly from time to time. It is vital that the controller algorithm of the 3D printer needs to be improved to deal with the errors that occur during bioprinting. In this study, a new adaptive PID controller based on Whale optimization Algorithm (WOA) is proposed for bio-printing of artificial tissues and organs. Thanks to the convergence speed and simplicity of the proposed metaheuristic optimization algorithm, the parameters of the PID controller can be adjusted quickly and accurately. The performance of the proposed WOA based PID controller was measured with the help of Integral of Time multiplied Squared Error (ITSE), one of the performance metrics. The proposed controller for the 3D printer is compared to the classical PID. From the results obtained, it is seen that the proposed controller significantly improves the transient response of the 3D printer.

The use of porphyrins in potentiometric sensors as ionophores

Abstract: Porphyrins are a group of macrocyclic compounds which are widely used in many applications in the literature due to their multiple functions. One of their application areas is in the development of sensors which transform changes in physical and chemical parameters into electrical signals. Novel chemical sensors selective and sensitive for many anions and cations have been fabricated with the advancements in the technology. Real sample applications of these sensors can be successfully performed. In recent years, potentiometric methods have drawn researchers' attention and they have been highly studied because of their superiority over other analytical devices. Ionophores which are used in the development of potentiometric sensors and ion-selective electrodes are the most important components of these systems. Therefore, porphyrin molecules, which have favorable chemical structures, can be directly used as an active component (ionophore) in the structure of potentiometric sensors. In this review, we investigated porphyrin derivatives used as ionophore in the design of potentiometric sensors and we focused on newly developed potentiometric sensors based on porphyrin.

The effect of Mentimeter and Kahoot applications on university students’ e-learning

Abstract: In the digital age, where technology is developing rapidly, there is a need for technology and game-based e-learning environments that students appreciate instead of traditional instruction Interactive Web 20 tools can be utilised to develop e-learning environments In this study, Kahoot and Mentimeter applications, interactive and game-based Web 20 tools, were used The effect of Kahoot and Mentimeter applications on e-learning was investigated This study was carried out at a state university in the Western Black Sea Region It was carried out with prospective teachers studying in the Department of Primary School Education This experimental study was conducted with 29 prospective teachers in the experimental group and 27 in the control group Attitude Scale Against e-Learning was applied to prospective teachers before and after the application Traditional methods were applied to the control group Kahoot’s evaluation feature and the word cloud feature of the Mentimeter program were used in the experimental group Keywords: e-Learning, Mentimeter, Kahoot, teaching, teaching technology

Applications of Potentiometric Sensors for the Determination of Drug Molecules in Biological Samples

Abstract: Potentiometry is extensively studied by researchers as one of the electrochemical methods due to its multiple advantages. Until today, thousands of potentiometric sensors have been developed and ap...

Effects of electromagnetic induction on vibrational resonance in single neurons and neuronal networks

Abstract: In this paper, Vibrational Resonance (VR), in which the response of some dynamical systems to a weak, low frequency signal can be enhanced by the optimal amplitude of high frequency signal, is investigated under the effects of electromagnetic induction in both single neurons and small-world networks. We find that the occurrence of VR in single neurons requires less energy in the presence of electromagnetic induction, although the resonant peak of the response reduces. Besides, VR can be obtained in small-world networks both with and without electromagnetic induction. In small-world neuronal networks, the highest resonance peak of VR enhances with an increase in the probability of adding link in case of without electromagnetic induction. On the other hand, with the increasing of the probability of adding link, VR disappears in the presence of relatively strong electromagnetic induction, while it enhances in the presence of relatively weak electromagnetic induction.

Error Probability Analysis of NOMA-Based Diamond Relaying Network

Abstract: Non-orthogonal multiple access (NOMA)-based cooperative relaying systems (CRS) are very promising to overcome spectral inefficiency of conventional cooperative communications. Although NOMA-CRS have great recent attention, almost all studies investigate NOMA-CRS only in terms of capacity and outage probability. Error performances of NOMA-CRS have not been well-studied. In this paper, we analyze error performance of NOMA-based diamond relaying network (NOMA-DRN) with imperfect successive interference canceler (SIC) as a NOMA-CRS scheme. We derive exact bit error probability (BEP) for NOMA-DRN and provide a tight approximated BEP in the closed-form. In addition, high-SNR analysis is conducted to present that NOMA-DRN has an error floor. Moreover, it is proved that NOMA-DRN turns out to be a non-equiprobable communication system and we derive priori probabilities of symbols. All derived expressions are validated via computer simulations.

Detection of Focal and Non-focal Epileptic Seizure Using Continuous Wavelet Transform-Based Scalogram Images and Pre-trained Deep Neural Networks

Abstract: Epilepsy is a neurological disease from which a large number of younger and older people suffer all over the world. The status of the patients is primarily examined by using Electroencephalogram (EEG) signals. The most important part for successful surgery is to locate the epileptic seizure in the brain. For this reason, it is very useful to detect the seizure area automatically before surgery. In this research, a novel method based on continuous wavelet transform (CWT) and two-dimensional (2D) convolutional neural networks (CNNs) has been proposed to predict focal and non-focal epileptic seizure. The AlexNet, InceptionV3, Inception-ResNetV2, ResNet50 and VGG16 pre-trained models have been used to automatically classify 2D-scalogram images into focal and non-focal epileptic seizure. The performances of 5 pre-trained models were compared and the detection results of 2D-scalograms were examined. The best classification accuracy of 92.27% is yielded by the InceptionV3 model among the other used four pre-trained models. As a result, it may be said that the pre-trained models and 2D-scalogram images of focal and non-focal EEG signals will be useful to neurologists for rapid and robust prediction epileptic seizure before surgery.

Error Probability Analysis of NOMA-based Diamond Relaying Network

Abstract: Non-orthogonal multiple access (NOMA)-based cooperative relaying systems (CRS) are very promising to overcome spectral inefficiency of conventional cooperative communications. Although NOMA-CRS have great recent attention, almost all studies investigate NOMA-CRS only in terms of capacity and outage probability. Error performances of NOMA-CRS have not been well-studied. In this paper, we analyze error performance of NOMA-based diamond relaying network (NOMA-DRN) with imperfect successive interference canceler (SIC) as a NOMA-CRS scheme. We derive exact bit error probability (BEP) for NOMA-DRN and provide a tight approximated BEP in the closed-form. In addition, high-SNR analysis is conducted to present that NOMA-DRN has an error floor. Moreover, it is proved that NOMA-DRN turns out to be a non-equiprobable communication system and we derive priori probabilities of symbols. All derived expressions are validated via computer simulations.

Development of Chromium(III)-selective Potentiometric Sensor by Using Synthesized Pyrazole Derivative as an Ionophore in PVC Matrix and its Applications

Abstract: A novel poly(vinyl chloride) membrane potentiometric sensor for chromium(III) ions based on the use of 5,5′-(1,4-phenylene)bis(3-(naphthalen-1-yl)-4,5-dihydro-1H-pyrazole-1-carbothioamide) as a neutral ionophore was developed. The optimum composition of the best performing membrane contained ionophore, potassium tetrakis (p-chlorophenyl) borate (KTpClPB), dibutyl phthalate (DBP), and poly(vinyl chloride) (PVC) in the ratio of 5.5:1.5:55:38 (mg). The sensor exhibits a working concentration range of 1.0 × 10–5–1.0 × 10–1 mol L−1 and a detection limit of 1.7 × 10–6 mol L−1. The sensor shows good selectivity for chromium(III) ions over a number of cations including alkali, alkaline earth, heavy and transition metals. The response time of the sensor is 8 s. In addition, the developed sensor shows good reusability and stability. The sensor operates in the wide pH range of 5.0–11.0. The sensor could be used as an indicator electrode in the quantification of Cr3+ ions by potentiometric titration against ethylenediaminetetraacetic acid (EDTA). Finally, this sensor was successfully used for the determination of chromium(III) in commercial water, purification water and wastewater.

A numerical treatment of the coupled viscous Burgers’ equation in the presence of very large Reynolds number

Abstract: A numerical investigation of the coupled viscous Burgers’ equation for very large Reynolds numbers do not exist in the literature. Coupled viscous Burgers’ equations are solved numerically in the presence of very large Reynolds numbers. For this case, numerical approach to the coupled viscous Burgers’ equation via contributions of two effective methods is used. The first component of the mixed method is finite difference method and the second one is differential quadrature method. For this process, the third order modified cubic B-spline functions are used as base function. To display the effectiveness of the present mixed method four different test problems have been investigated. For various values of the coefficients, more particularly for very large Reynolds numbers, in other words, for the very small value of kinematic viscosity parameters solutions are obtained. Error norms are calculated and compared with analytical results and also with numerical results of the related literature. Present results display that the present mixed method obtains high accurate solutions and in compatibility with both of the analytical and numerical results.

Synthesis, Docking Studies and Biological Activity of New Benzimidazole- Triazolothiadiazine Derivatives as Aromatase Inhibitor.

Abstract: In the last step of estrogen biosynthesis, aromatase enzyme catalyzes the conversion of androgens to estrogens. Aromatase inhibition is an important way to control estrogen-related diseases and estrogen levels. In this study, sixteen of benzimidazole-triazolothiadiazine derivatives have been synthesized and studied as potent aromatase inhibitors. First, these compounds were tested for their anti-cancer properties against human breast cancer cell line (MCF-7). The most active compounds 5c, 5e, 5k, and 5m on MCF-7 cell line were subject to further in vitro aromatase enzyme inhibition assays to determine the possible mechanisms of action underlying their activity. Compound 5e showed slight less potent aromatase inhibitory activity than that of letrozole with IC50 = 0.032 ± 0.042 µM, compared to IC50 = 0.024 ± 0.001 µM for letrozole. Furthermore, compound 5e and reference drug letrozole were docked into human placental aromatase enzyme to predict their possible binding modes with the enzyme. Finally, ADME parameters (absorption, distribution, metabolism, and excretion) of synthesized compounds (5a-5p) were calculated by QikProp 4.8 software.

Threshold-Based Selective Cooperative NOMA: Capacity/Outage Analysis and a Joint Power Allocation-Threshold Selection Optimization

Abstract: In this letter, we analyze ergodic capacity (EC) and outage probability (OP) for threshold-based selective cooperative non-orthogonal multiple access (TBS-C-NOMA) which has been proposed as a reliable alternative to conventional C-NOMA networks. We derive exact expressions for EC and OP in closed-forms and the theoretical analyses are validated via computer simulations. The trade-off for TBS-C-NOMA is raised where the data reliability (i.e., error rate) changes reversely with the EC and OP performances. Nevertheless, it is proved that TBS-C-NOMA provides a remarkable gain in data reliability compared to the decay in EC and OP. Finally, the optimization of power allocation (PA) is re-defined considering the trade-off in TBS-C-NOMA and a joint PA-threshold selection (TS) optimization is proposed in terms of capacity maximization under required data reliability.

Synthesis, anticancer evaluation and molecular docking studies of new benzimidazole- 1,3,4-oxadiazole derivatives as human topoisomerase types I poison

Abstract: In this study, some benzimidazole-oxadiazole derivatives were synthesised and tested for their in vitro anticancer activities on five cancer cell lines, including HeLa, MCF7, A549, HepG2 and C6. Th...

The relationship between nurses' empathic tendencies, empathic skills, and individualized care perceptions.

Abstract: Purpose The study aims to determine the relationship between nurses' empathic tendencies, empathic skills, and individualized care perceptions. Design and methods This study employed a descriptive and correlational design. Data were collected from nurses in eight training and research hospitals in Turkey. The study sample consisted of 472 nurses who were both volunteered to participate and randomly selected. Findings A definite high-level relation was determined between nurses' empathic tendencies and individualized care perceptions, but no relation was determined between nurses' empathic skills and individualized care perceptions. Practice implications This study can underline practices accounting for empathy and individualized care in nursing research and training programs.