Bülent Ecevit Üniversitesi

In this study, the operational conditions of a low‐cost in‐house‐built single screw filament extruder including operational parameters of extruder temperature (170–190°C) and screw speed (20–40 rpm) were evaluated in terms of the quality and dynamic‐mechanical properties of extruded PLA filaments. The designed experimental parameters were used to investigate the impact of operational parameters on filament diameter by Taguchi model where the predicted responses were evaluated. Results showed superior performance with R² value of 0.9898. The measured filament diameters validated the predicted responses, and the error percentage was in the range of 0%–2.1%. Furthermore, in order to classify and distinguish filaments, we adept the artificial neural network (ANN) model classifier, and the measured dynamic mechanical analysis (DMA) values at certain temperatures were taken into consideration where the classes they belonged to be determined. Results reveal successful classification accuracy of filaments in the range of 94.82%–99.98% depends on different cases of classification process.

Background Although problem based learning has a great potential to promote learning outcomes as well as cognitive abilities, the number of previous studies investigating this potential on critical thinking, reading comprehension abilities and attitudes in English as a Foreign Language (EFL) context is scarce. Also, the number of studies comparing the effectiveness of online and face‐to‐face problem based learning on these variables is scarcer and this limited research has yielded contradictory results. Objectives This study aimed to investigate and compare the effectiveness of online and face‐to‐face problem based learning on critical thinking skills, dispositions, reading comprehension abilities, and attitudes of EFL students. Method Pretest‐posttest control group quasi‐experimental design was employed in this study that has two experimental groups and one control group. There were 23 (13 females and 10 males), 23 (14 females and 9 males), and 22 (12 females and 10 males) EFL students in online problem based learning group, face‐to‐face problem based learning group, and control group, respectively. In the first experimental group, problem based learning activities were conducted online while the same activities were conducted face‐to‐face in the classroom in the second experimental group. In the control group, no experimental procedures were followed and the students continued their regular instructor‐led reading activities. Watson‐Glaser Critical Thinking Test, Sosu Critical Thinking Dispositions Scale, Reading Comprehension Test, and Attitudes Towards English Scale were used to collect the data. The data were analysed using paired‐samples t‐test and ANCOVA with pretest scores as the covariate variable. Results and Conclusions It was found that problem based learning, either online or face‐to‐face, significantly enhanced EFL learners' reading comprehension abilities and attitudes towards English as well as their critical thinking skills and dispositions. It was also concluded that online problem based learning is more effective to enhance reading comprehension abilities and attitudes than face‐to‐face problem based learning while they are equally effective in promoting critical thinking skills and dispositions in EFL classroom. Implications This study has significant implications for educators and researchers. As this study showed that online problem based learning is a promising alternative to face‐to‐face problem based learning, universities, educators, administrators, and researchers can organize their problem based learning activities online to promote learning outcomes and cognitive abilities in language classrooms. Also, future studies employing qualitative or mixed methods can be conducted with sample groups from different educational levels to provide an in‐depth examination of the advantages and disadvantages of online and face‐to‐face problem based learning.

Objectives Excessive fructose consumption is recognized to elevate insulin resistance in animals and humans. In our study, we aimed to assess the possible consequences of curcumin (curc) treatment applied to rat models of fructose-induced insulin resistance on adenosine monophosphate-activated protein kinase (AMPK) and phosphatidylinositol-3 kinase (PI3K)/protein kinase B (Akt) pathways in skeletal muscle and adipose tissue. Methods We established four distinct rat groups: corn oil (negative control group), 20 % fructose (positive control group), 20 % fructose and 100 mg/kg curc (100 mg/kg curc group), and 20 % fructose and 200 mg/kg curc (200 mg/kg curc group). The ELISA method was used to determine serum insulin levels, an auto-analyzer was used to measure serum glucose levels, and homeostatic model assessment of insulin resistance (HOMA-IR) values were calculated. In the rat’s skeletal muscle and adipose tissues, the ELISA method was used to determine the following parameters: insulin receptor substrate-1 (IRS-1), phosphorylated insulin receptor substrate-1 (p-IRS-1), PI3K, phosphatidylinositol 3,4,5-trisphosphate (PIP3), phosphoinositide-dependent kinases (PDK-1), phosphorylated Akt (p-Akt), AMPK and glucose transporter type 4 (GLUT4) levels. Results The positive control group exhibited a significant increase in serum glucose, insulin, and HOMA-IR levels, confirming the establishment of the insulin resistance model. In the curcumin dose groups, these values significantly decreased. Additionally, compared to the positive control groups, curcumin dose groups demonstrated a significant increase in the parameters of the Akt/PI3K pathway, AMPK activation, and GLUT4 levels in skeletal muscle and adipose tissues. Conclusions We observed that curcumin demonstrates potential ameliorative effects on the insulin signaling pathway through PI3K/Akt and AMPK pathways.

This paper aims to study a discrete-time COVID-19 epidemic model with a saturated incidence rate. The basic reproductive number is calculated and the endemic steady state is obtained for the model. The stability of the COVID-19-free steady state (CFSS) of the model is investigated when the basic reproduction number is less than one and the step size h satisfies the exact condition. The theoretical result is also supported with numerical simulations.

Although large‐scale pretrained convolutinal neural networks (CNN) models have shown impressive transfer learning capabilities, they come with drawbacks such as high energy consumption and computational cost due to their potential redundant parameters. This study presents an innovative weight‐level pruning technique that mitigates the challenges of overparameterization, and subsequently minimizes the electricity usage of such large deep learning models. The method focuses on removing redundant parameters while upholding model accuracy. This methodology is applied to classify Eimeria species parasites from fowls and rabbits. By leveraging a set of 27 pretrained CNN models with a number of parameters between 3.0M and 118.5M, the framework has identified a 4.8M‐parameter model with the highest accuracy for both animals. The model is then subjected to a systematic pruning process, resulting in an 8% reduction in parameters and a 421M reduction in floating point operations while maintaining the same classification accuracy for both fowls and rabbits. Furthermore, unlike the existing literature where two separate models are created for rabbits and fowls, this article presents a combined model with 17 classes. This approach has resulted in a CNN model with nearly 50% reduced parameter size while retaining the same accuracy of over 90%.

This study aims to reveal pre-service teachers’ experience in virtual museum design that they can use in social studies teaching, and their opinions on virtual museum applications. In line with this purpose, phenomenology design was used as one of the qualitative research approaches. Selected by the criterion sampling method, the study sample consisted of a total of 15 pre-service social studies teachers (9 female, 6 male) who were studying in year 4 at the Department of Social Studies Education of a State University in the 2021/22 academic year. During the 9-week virtual museum design process, virtual museums on “epidemics, women’s rights, population, environmental problems, climate, human rights, and migration” were designed through the Artsteps application. The study was executed in a dynamic manner in co-operation and interaction with pre-service teachers based on the principles of design, implementation and evaluation. A semi-structured interview form was used as a data collection tool to determine the opinions of pre-service teachers about virtual museums and the use of virtual museums in social studies teaching. The data was analysed by content analysis. The results revealed that the virtual museum design process positively affected the views of pre-service teachers and that virtual museums are very effective and applicable tools in social studies teaching. This study suggests that virtual museums be used in social studies courses since they offer rich content to achieve meaningful learning in social studies courses owing to easy accessibility, and that future studies focus on examining the effects of popularizing virtual museums designed with gamification and guided content.

Nanofluids exhibit remarkable thermophysical properties, making them highly promising candidates for heat transfer applications. Viscosity is a crucial property among the thermophysical properties of nanofluids, significantly influencing heat transfer rates and pressure loss computations. In this study, the dynamic viscosity of water-based nanofluids containing Al2O3, TiO2, and ZnO nanoparticles was experimentally measured over a wide range of volumetric concentrations (0.1–1.0%) and temperatures (20–50 °C). Then, the dynamic viscosity of nanofluids is predicted with a multi-layer perceptron artificial neural network (ANN). Moreover, the genetic algorithm (GA) is adopted for obtaining the dynamic viscosity value of nanofluids. Finally, the results obtained from the designed ANN model and GA are compared. The results show the feasibility of predicting the dynamic viscosity with the designed ANN model. The proposed ANN model holds promises to meet demands for the detection of the dynamic viscosity of the nanofluids instead of using theoretical estimation equations or experiments which require substantial expertise or time.

Solvent-based CO2 capture is a commonly employed post-combustion technique in processes involving absorber-stripper columns. This study focused on computer simulations with equilibrium- and rate-based modeling of CO2 capture using the amine solvents 2-amino-2-methyl-1-propanol (AMP), diethanolamine (DEA), and methyl diethanolamine (MDEA) and thermodynamic methods involving electrolyte NRTL models. The objective of this study was to understand the impacts of rate-based modeling, the type of amine, and thermodynamic methods on carbon capture. Within this study, the amine-based CO2 capture process from coal-power plant flue gas was studied using Aspen Plus modeling. Simulations were also conducted to determine the impact of thermodynamics and kinetics on the CO2 capture performance of the system. The results were analyzed on the basis of captured CO2 according to the solvents and models. The equilibrium approach was mostly invalid because of the oversimplified ideal stage assumptions through the column. The lowest carbon capture capacity was obtained with MDEA, while DEA yielded the best results. A sensitivity analysis with rate-based modeling showed the significant impact of the inlet CO2 composition. The amine-based CO2 capture process simulation included solution chemistry, electrolyte thermodynamics, rigorous transport property modeling, reaction kinetics, and rate-based multistage simulation, which could be applicable to different solvent systems.

Background In this study, the antimicrobial activity of three different cleanser tablets on S. mutans and C. albicans adhesion to PMMA, polyamide and 3D printed resin was investigated. Methods 40 samples were prepared for PMMA (SR Triplex Hot), polyamide (Deflex) and 3D printed resin (PowerResins Denture) materials and divided into four subgroups for cleansers (Aktident™, Protefix™, Corega™ tablets and distilled water) (n = 5). After the surface preparations were completed, the samples were immersed separately in tubes containing the prepared microorganism suspension and incubated at 37˚C for 24 h. After the incubation, the samples were kept in the cleanser solutions. The samples were then transferred to sterile saline tubes. All the tubes were vortexed and 10 µl was taken from each of them. Sheep blood agar was inoculated for colony counting. The inoculated plates were incubated for 48 h for S. mutans and 24 h for C. albicans. After incubation, colonies observed on all plates were counted. Statistical analyses were done with three-way ANOVA and Tukey’s multiple comparison test. Results Polyamide material registered the highest colony count of S. mutans, whereas PMMA registered the lowest. Significant differences in S. mutans adherence (p = 0.002) were found between the three denture base materials, but no such difference in C. albicans adherence (p = 0.221) was identified between the specimens. All three cleanser tablets eliminated 98% of S. mutans from all the material groups. In all these groups, as well, the antifungal effect of Corega™ on C. albicans was significantly higher than those of the other two cleanser tablets. Conclusions According to the study’s results, it may be better to pay attention to surface smoothness when using polyamide material to prevent microorganism retention. Cleanser tablets are clinically recommended to help maintain hygiene in removable denture users, especially Corega tablets that are more effective on C. albicans.

In this study, chestnuts were processed into flour, and the obtained chestnut flour (CF) was passed through a sieve with an aperture size of 212 µm. Then the physicochemical properties, digestible and resistant starch contents of CF1 (<212 µm), CF2 (≥212 µm), and commercial chestnut flour (CCF) were determined. It was found that CCF had the highest values in terms of proximate composition, total soluble polyphenol content (891.25 mg GAE/100 g), and antioxidant activity (ABTS: 1552.11 mg TE/100 g, DPPH: 2003.01 mg TE/100 g). On the other hand, CF1 was superior in terms of resistant starch content (39.31 g/100 g, dw) ( p < 0.05). The resistant starch content of CF1 was approximately 1.5-fold and 3-fold higher than CCF and CF2, respectively. Furthermore, the rapidly digestible starch content of CF1 (2.1 g/100 g, dw) and CF2 (0.93 g/100 g, dw) was quite lower than CCF (12.64 g/100 g, dw) ( p < 0.05). Moreover, CF1 exhibited lower ( p < 0.05) water, alkaline water, and sodium carbonate retention capacities, which make it a potential good flour for cookie and cracker production. In contrast, CF2 could be evaluated as a good ingredient for noodle-type foods due to its lower water solubility index. Considering the least gelation concentrations of samples, it was seen that CF1 (% 10) could also be valorized as a thickening or gelation agent in the food industry, as well as CCF (% 6).

Five different foam concretes were synthesized and examined. A new hybrid optical sensor, called combined digital holographic microscopy (CDHM), was proposed by combining microscopic fringe projection profilometry and lateral shearing digital holographic microscopy to detect the pore radii of produced foamed concretes. It was applied in addition to SEM and has not been applied to foam concretes before. Thanks to the proposed method, it was revealed that the measured CDHM radii contained a relative error of less than 6% compared to the SEM radii. The pore radii increased as the % of foaming agent used in the samples increased. Accordingly, the sample densities decreased and thermal insulation properties enhanced. Two-layer quantum chemical calculations performed at the ONIOM (M06-2X/6-31+G(d,p):UFF) theoretical level showed that thermodynamic stability of foam concretes increased as the % of foaming agent used, or more precisely, the pore radius, increased. The CDHM method provides results close to SEM and has superior features such as being more cost-effective, cleaner and faster. For this reason, it is thought that the proposed method will lead to future studies in terms of measuring pore radii as an alternative to SEM. Graphical Abstract The combined digital holographic microscopy (CDHM) method is proposed as an alternative to SEM with a relative error of less than 6% in determining the pore radius of foam concretes.