In this study, AISI-430 and HARDOX 500 steels were welded with Cold Metal Transfer (CMT) welding technique, and the changes in the mechanical properties in the welding and HAZ (Heat Affected Xone) regions were investigated. 100x130x6 mm AISI 430 and HARDOX 500 steels were cut in standard sizes with a band saw and joined by the CMT method using AWS 307 additional wire. The mechanical properties and microstructural changes of the welded areas were investigated by various analyses, microhardness, notch impact, and tensile tests, and the ruptured surfaces of the test specimens after the tensile test were investigated by SEM analysis. Welding was carried out at 140 A, 130 A, and 120 A currents. 97.5% Argon and 2.5% CO2 gas were used as shielding gas. It has been determined that there are differences in morphology from the optical images after welding. Coarse grains were formed in the HAZ regions but were limited to the low-temperature input of the CMT welding. According to the EDS analysis results, it has been determined that there are atomic transitions between the regions. In the hardness analysis, there was a slight decrease in hardness in the HAZ regions compared to the base metals. In the tensile test, all 3 samples broke from the AISI 430 main material part. Elongation amounts were measured between 16.81 and 17.90 mm, and tensile strengths were measured between 417 and 441 MPa. As a result of the study, it has been revealed that the mechanical properties of AISI 430 and HARDOX 500 steels combined with CMT Welding have increased significantly in the weld zone and weldability is possible.

The aim of this study is to investigate the effects of nano calcite (nano-CaCO3) particles on the mechanical properties of carbon fiber/epoxy (CF/EP) composites manufactured by vacuum-infusion process. For this aim, nano calcite (nano-CaCO3) particles at different loading ratios (1wt. %, 3wt. % and 5wt. %) were integrated into the epoxy matrix by pre-dispersion method. Then, reference and nano-CaCO3 reinforced CF/EP composites were manufactured by vacuum-infusion method. Short-beam shear, end-notch flexure (ENF) and Charpy impact tests were carried out on the prepared composite test specimens according to relevant ASTM standards. Additionally, the thermomechanical response of the composite specimens was determined via dynamic mechanical analysis (DMA). The fractured surfaces were examined by scanning electron microscopy (SEM). The results showed that the nano calcite particles could improve interlaminar shear strength (ILSS), Mode-II fracture toughness and Charpy impact strength by 17.4%, 34.1% and 10.0%, respectively, compared to the reference CF/EP composites. For these loading conditions, the optimum nano-CaCO3 amounts were determined as 1%, 5% and 3%, respectively. DMA results showed that the nano-CaCO3 particles had no significant effect on the glass transition temperature (Tg) of the composites.

In the field of electronics, technological advancements bring about alterations in electronic devices. With the fast-paced advancements in the era of communication, the frequency bands are becoming congested. One of the frequencies utilized by Wi-Fi-6, 6 GHz, has garnered its share of attention in these changes. This study proposes the design of a Butterworth Bandpass Filter that can be utilized in the IEEE C Band, employing both lumped elements and microstrip transmission lines. The methodology for determining the length and width of the transmission lines, as well as the considerations taken into account during their modification, is provided. A composite filter comprising of both elements and lines is also presented. The combined filter design is aimed at resolving issues arising from the increasing frequency. During the design process, an 8th-order lowpass filter is generated using the Butterworth normalized table, and a bandpass filter with a 200 MHz bandwidth and a 6 GHz center frequency is obtained through the use of a resonant circuit. The dimensions of the transmission lines are determined using theoretical formulas and verified through Matlab files and the MWO-AWR optimization tool. The voltage gain graphs generated demonstrate that the designs created on an FR4 substrate are suitable for use in the IEEE C Band for Wi-Fi-6.

The biological neuron models, which have the biologically significant, describe the characteristics of neurons in the living body. These models can be defined similar to oscillators. A great of the theorems that describe the characteristics of oscillator structures, such as stability control and synchronization control, can also be used to examine the biological neuron models. Recently, the rotation-transition process has become a remarkable issue in the nonlinear dynamical system applications. After the rotation-transition process; the dynamical attractor of a nonlinear system can be directed to any desired direction by changing the rotation angle. One of the most known examples of the nonlinear dynamical systems is the chaotic oscillator structures. There are many studies on the dynamical attractor control of the chaotic oscillators by means of rotation-transition in the literature. However; although the rotation changes are observed in the dynamical characteristics of the real biological systems, there isn’t any study dealing with the rotation controls of the dynamical attractors of biological neuron models. Therefore, the rotation-transition procedure of the Fitzhugh-Nagumo (FHN) model has been handled in this study. The equilibrium points of the rotated FHN neuron model are calculated for getting its characteristic outputs. After the rotation-transition process, the changes on the rotation of the dynamic attractors of the FHN neuron have been observed by numerical simulation results. Finally, the rotated-controlled FHN neuron has also been realized with the ‘Field Programmable Gate Array- (FPGA)’, which is a programmable and reconfigurable device, in order to both support the functionality of the rotation transformation process and to obtain the real-time signals requiring for the bio-inspired systems. Thus, it has been shown that thanks to the proposed rotation-transition process, the phase adjustment of the system dynamics in neural systems can be intervened without requiring any coupling definition. Based on this view; the mathematical descriptions of the rotated-FHN neuron model has been pointed out, this model is promoted by the numerical simulations and confirmed by the hardware implementation studies.

Micro Electro-Mechanical System (MEMS) Based Inertial Measurement Units (IMU) are widely used for attitude estimation in unmanned aerial vehicle (UAV) systems owing to their small, light weight and cost effectiveness. On the other hand, it has some disadvantages that influence performance, such as noisy output, low sensitivity, poor accuracy, and bias stability. Also, MEMS-based IMU sensors (accelerometers and magnetometers and gyroscopes) cannot provide adequate navigation solutions as a standalone system. Different sensor fusion techniques have been proposed in the literature to obtain reliable attitude estimation. However, most of these fail in situations such as nonlinear measurement models, nonlinear process dynamics, and long-range navigation. This article presents a new fuzzy rule-based complementary filter (CF) that combines magnetic field, angular velocity and acceleration measurements from low-cost MEMS-based IMU sensors to achieve a more robust attitude prediction in a UAV under dynamic motion. The proposed approach adjusts the cut-off frequency of the CF to the optimum value according to the variable dynamic motion of the system. Thus, the problem of constant cut-off frequency is eliminated and a more robust attitude prediction is achieved even with the varying movements of the system. Both real experiments and numerical simulations confirm the validity of the presented method.

Understanding Protein - Protein interaction networks, which show the interactions between proteins involved in tasks that are very important for our organisms such as structural support, storage, signal transduction and defence, provides a better understanding of cellular processes. One of the important studies carried out for this purpose is to try to detect protein complexes from protein - protein interaction networks. Supervised and unsupervised machine learning methods were used to detect protein complexes. It is known that the machine learning methods used produce better performance when more than one method is used together. Based on this knowledge, a method that detects protein complexes from protein-protein interaction networks is proposed in this study. The method first weights protein-protein interaction networks using biological and topological properties of proteins. Then it estimates local and global protein complex core. Then it builds a protein complex detection model using the structural modularity of proteins and the voting regression model. We predict that XGB regression, gaussian process regression, catboost regression and histogram-based gradient boosting regression supervised learning methods can achieve more successful results when used together in the voting regression model. When we compare the success of the model with other models, it has shown the best performance many times among the compared models.

Scientifically, the efficiency of a method refers to its power to best predict/calculate based on an evaluation following a certain process within the current scenario, parameter and/or data. For a good prediction, the most appropriate approach(es) to a problem should be considered and the related tests should be done reliably. Practical studies in the field of food safety and fruit quality are critical, with the accuracy, speed and economic parameters of the methods used being of particular importance. In this study, for the first time in literature an Arduino-based temperature and gas monitoring system (called e-nose) is used to monitor the decay of avocado fruit in a controlled experimental environment and support vector machines, a machine learning method, are used to detect (classification) the decay. In this study, test and validation success of over 98.5% was achieved with very few training-data for classification. The obtained results are encouraging in terms of the detection results of the developed e-nose and the method used to determine the level of decay in other fruit in cold storage.

Sepsis, which is known as multiple organ failure, is the primary cause of mortality for all patients in intensive care units, regardless of their other illnesses. An intensive care unit decision support system that can predict sepsis in intensive care patients early and warns the doctor has been developed. Since the COVID-19 virus, the variant and number of intensive care patients have increased, so this study has been developed as a precaution to worsen the situation with sepsis. A user-friendly interface and system have been designed to help the physician better monitor the patient's sepsis status. It has been developed in order to meet the need for a decision support system that makes sepsis estimation in accordance with the reference intervals of Turkish patients' values. For a better result of predicting sepsis early, it has been concluded how the data obtained and used in a certain period of time should be analyzed and what methods could be used to estimate higher performance. In the study, machine learning (classification and regression), deep learning algorithms have been used for estimation and the results obtained have been compared. As an impact of research, an intensive care sepsis decision support system, which consists of 122400 hourly data of 300 intensive care patients and estimates with approximately between 88% and 94% successful results in accordance with the reference intervals of Turkish patients, has been developed.

Diagnosis of anomalies in the gastrointestinal tract is a current research area. Wireless capsule endoscopy (WCE) for the evaluation of this region is a preferred alternative technology to avoid the risks of traditional endoscopy and to provide a painless process. But this technology, which has many advantages, offers low frame density. This situation affects the quality of the data and causes to a decrease in the diagnostic accuracy rate. In this study, WCE endoscopy images obtained from KID Atlas Dataset 2 were used and a three-stage artificial intelligence-supported diagnostic process was developed for the detection of the inflammatory anomaly, vascular anomaly, polypoid anomaly and normal image categories in the gastrointestinal tract. For the first stage, critical points on the images were clarified using 5 different approaches. These improved images were classified with a region proposal-based object recognition algorithm and performance comparison was made according to the approaches used. In the second stage, the data augmentation technique was applied to the improved images that showed maximum performance in the first stage. Thus, a dataset with a balanced and sufficient number of images was created. In the third stage, this current dataset was classified with five different object recognition algorithms. However, the individual success of each algorithm is different. For this reason, the ensemble learning approach was used to obtain stable outputs for each category and to create a balanced detection process among the categories. Finally, a balanced and stable estimation function was provided between categories with this hybrid structure.

In this study, cantilever retaining walls constructed near the highways were investigated according to real project values. Twenty-eight retaining wall projects applied in the site at different regions of Turkey such as Central Anatolia, Marmara and Black Sea were considered. The value of surcharge load, depth of foundation, ground water level, surface slope of soil and wall height were chosen as variable parameters, although properties of base soil, granular backfill and natural soil were considered as constant parameters. Theoretical calculations of factor of safeties were completed against overturning, sliding and bearing capacity according to each case as well as two-dimensional finite element models were solved in Plaxis software to find the maximum horizontal deformations. Rankine active and passive earth pressure theories were used to make static analysis of cantilever walls. If the surcharge load, surface slope of soil, height of wall and ground water level increases, the stability conditions depending on factor of safeties decreases due to results. In addition, a deeper depth of foundation increases the factor of safeties against sliding and bearing capacity, while it does not affect the overturning behavior. The location of ground water stands out as a dominant parameter rather than other external factors. Therefore, the design height of reinforced concrete cantilever retaining wall is not proposed in which taller than 15m due to unsecure and uneconomical conditions, even if other criteria are met.

In this study, the mechanical and transport properties of concrete mixtures containing two raw perlite aggregates, obtained from two different quarries in Turkey, were compared with those of the concrete produced with crushed limestone aggregate. XRD analysis of the perlites showed that the aggregates contain roughly similar crystal components. However, Erzincan perlite was found to contain more amorphous phases. Moreover, water absorption of Ýzmir perlite ranged from 7.25 to 12.45% which was far above that of the Erzincan perlite with water absorption ranging from 5.08 to 7.45%. However, the 100 and 500 cycles Los Angeles weight losses revealed that the aggregates had a sufficient and uniform hardness. Within the scope of the study, 9 concrete mixtures with three different water/cement ratios (i.e., 0.69, 0.56 and 0.41) were prepared. It was found that the unit weights of the concretes produced with raw perlite aggregate remained within the limits of lightweight concrete and were 15-21% lighter than those of the control concretes. Moreover, the Erzincan perlite-bearing concretes showed 12-41% lower compressive strength that those of the control mixtures. The lowest performance in terms of either mechanical or transport properties were obtained in the Ýzmir perlite aggregate-bearing concrete mixtures.

Harvesting energy from the salinity gradient of seawater and river water using pressure retarded osmosis (PRO) has been a major research topic of recent years. However, because the performance of existing membranes on the market is poor, there is a need for effective PRO membranes with high power density and pressure resistance. In this study, specific energy potential of PRO process using L-DOPA+TiO2 modified BW30-LE membrane was evaluated on synthetic and real water samples. Polyamide BW30-LE RO membrane was modified by L-DOPA, L-DOPA+0.5 wt% TiO2 and L-DOPA+1 wt% TiO2. The effect of hydraulic pressure and temperature on power densi-ty were evaluated for 5, 10, and 15 bar pressures, as well as 10 °C, 20 °C, and 30 °C degrees. The incorporation of TiO2 nanoparticles with L-DOPA increased the water flux by in-creasing the surface hydrophilicity and roughness of the membrane surface. The maximum specific power was ob-served as 1.6 W/m2 for L-DOPA+1 wt% TiO2 modified BW30-LE membrane at 15 bar pressure. Besides, Mediterranean and Aegean, Black Sea water samples were used as draw solution and Seyhan, Ceyhan, Buyuk Menderes, Gediz, Yesilirmak, and Kizilirmak Rivers were used as feed solution. The highest osmotic power density was obtained by using BW30-LE/L-DOPA+1 wt% TiO2 membrane with Ceyhan River as feed and Mediterranean Sea water as draw solution, which have the highest differences in salinity. In the mixture of Mediterranean and Ceyhan River, the highest power density was obtained at 10 bar pressure at 30 ± 5°C with 0.70 W/m2.

In this study, solid waste clays of Emet Concentrator Plant were used. With the data obtained from the characterization tests, it was decided to work with the sample fraction with Li grade in the range of 1942-2035 ppm, which is below 0.5 mm grain size. Clay sample and salt mixtures are blended at different rates; In the first stage, the leaching effect of salt type and ratio, and in the second stage the roasting time and temperature were investigated. The effectiveness of the parameters used in the roasting study was evaluated by lithium extraction studies. In the third phase of the study, leaching, the study was completed by considering the H2SO4 concentration and leaching time. With the processes carried out, first of all, the mixture obtained by blending the sample/salt mixture ratio of 1/1 and between the salts in the ratios of NaCl (0.6) – CaCl2 (0.4); Result of roasting at 900°C for 1 hour, a roasting efficiency of 77.26% Li was obtained. Then, by leaching the roasted material in the presence of 0.4M H2SO4 for 1 hour, the leaching efficiency was determined as 89.61% Li.

In this study, polyaniline (PANI) polymerization was carried out on the surface of graphene oxide (GO) nanosheets by using AgNO3 as an oxidation agent, and GO-Ag-PANI three-component nanocomposite was obtained with a one-step and simple method. As a result of the simultaneous PANI polymerization and Ag nanoparticle formation processes on the surface of the GO nanosheets, the GO surface was homogeneously coated with PANI, and Ag nanoparticles were successfully produced in the PANI polymeric matrix. The prepared nanocomposite has high colloidal stability and strong absorbance in the NIR region. Depending on the photoagent concentration (0.025, 0.05 and 0.1 mg/mL) different laser power densities (1.0, 1.5 and 2.0 W/cm2) were applied at a wavelength of 808 nm, the aqueous dispersion of GO-Ag-PANI exhibited extraordinary maximum temperature difference values and showed a high photothermal conversion efficiency (45.9%). In addition, it was determined that the nanocomposite, which was subjected to repeated heating-cooling cycles, had very high photostability. GO-Ag-PANI three-component nanocomposite with high photothermal performance has great potential for photothermal applications.