The effectiveness of generative artificial intelligence models in software development is determined not only by their ability to generate correct solutions but also by their adherence to quality metrics and their resilience to exceptional scenarios. In this context, a comparative evaluation was conducted on four models using 10 fundamental algorithm problems and 10 object-oriented programming problems in the C# programming language. The generated solutions were assessed in terms of time complexity, memory usage, lines of code, number of variables and methods, and execution time. In addition, meaningful edge-case scenarios were employed to measure error tolerance and exception handling performance. The findings indicate that all models produced functionally valid solutions, yet exhibited limitations in advanced software engineering practices such as modularity, comprehensive error management, performance measurement, and unit testing. The analysis revealed that ChatGPT and Gemini stood out in terms of structure and consistency, Claude demonstrated greater reliability in handling errors, while Copilot offered advantages in code simplicity. Overall, the results highlight the importance of evaluating generative AI models not only under ideal conditions but also in atypical scenarios to ensure software quality and reliability.

Offshoring location selection is a crucial decision for firms in terms of competitiveness, flexibility, productivity, and profitability. Determining an efficient and appropriate location for offshoring has been a substantial multicriteria decision-making (MCDM) problem. Considering that the outcome of an MCDM method alone can be misleading, a novel hybrid approach is presented in this study. Thus, five MCDM methods are utilized to solve the problem, and the results of four MCDM methods are integrated to assure an optimal offshoring location. A Fuzzy-AHP (analytical hierarchy process) integrated with the technique for order preference by similarity to ideal solution (TOPSIS), additive ratio assessment (ARAS), elimination et choix traduisant la realité (ELECTRE), and weighted sum method (WSM) methodology is proposed for the appraisal and selection of the optimal offshoring location. In this context, fifteen alternative locations are determined based on the attractiveness of the locations in terms of offshoring. Fuzzy-AHP is implemented to analyze the problem's structure and find the weights of the quantitative and qualitative criteria. Consistency tests are implemented to assess the quality of inputs of an expert. Then, TOPSIS, WSM, ARAS, and ELECTRE are used to evaluate and rank the candidate locations and present a comparative analysis. By considering fifteen countries and using real data, offshoring location selection is conducted through the proposed methodology. Moreover, sensitivity analysis is made to diminish the subjectivity and assess the robustness of the techniques. The results demonstrated that giving more weights to the labor characteristics and proximity to market criteria might improve the quality of the best offshoring country index.

Hazelnut shell biosorbent immobilized on Amberlite XAD-4 polymer resin as solid phase extraction method was developed and used for preconcentration of Cu(II) and Cd(II) ions from aqueous solutions. Concentrations of analytes in solutions were determined by Flame Atomic Absorption Spectrometry after extraction with column technique. Functional groups of nutshell biosorbent immobilized on resin were characterized by Fourier transform infrared spectrometry. Optimized critical parameters for preconcentration of Cu2+ and Cd2+ ions from sample solutions with nutshell immobilized on the resin were the pH value of solution, type of eluent solutions, the flow rate of sample solution, quantities of nutshell biosorbent, Amberlite XAD-4 resin, and enrichment factors, respectively. Detection limits of Cu2+ and Cd2+ ions found were 0.29 μg L-1 and 0.25 μg L-1, respectively. The method proposed was applied for determinations of Cu2+ and Cd2+ ions in standard reference material (BCR-670 aquatic plant sample) for accuracy and applied to real water samples such as wastewater and Van lake water. At the 95% confidence level, relative standard deviations (RSDs) were found as 1.44% for Cd2+ and 1.21% for Cu2+ ions with three replicate measurements.

Nanoparticle studies are groundbreaking today, largely due to unpredictable changes in particle size and surface properties. Therefore, nanoparticles are considered as building blocks in optoelectronics, pharmaceuticals, nuclear energy, bioengineering, biomedicine and industrial applications. Today, the importance of environmentally friendly methods is increasing. The use of the green synthesis method, which adopts an economic synthesis approach that will reduce resource and energy consumption and do not harm the environment, is also encouraged in every field. In the study, biosafe ascorbic acid was used as an alternative reagent (agent) to the chemical reduction method. The method process performed with the reagent selected for nanoparticle synthesis has ensured that it is green synthesis, which is adopted as non-toxic and environmentally friendly. In this study, nanoparticles were synthesized by reducing the sulphate, nitrate, chloride and acetate salts of Cu(II), Ni(II), Co(II), Zn(II) and Mn(II) transition metals with the reducing agent ascorbic acid compound. It is aimed to investigate the effects of the same metal cations and different anion salts on nanoparticle synthesis. Depending on the radius ratios and solubility values of metal cations and anions, the nanoparticle obtained from Ni(CH3COO)2 salt has the smallest radius. Nano metal particles with the largest radius were obtained as a result of reduction from Co(NO3)2 salt. The characterization of the synthesized nanoparticles were recorded by particle size analysis and scanning electron microscopy (SEM) images.

Emissions from vehicles and other fuel combustion systems can alter the composition of the atmosphere and augment its capacity to absorb heat. These gases, which are effective at trapping heat, are known as greenhouse gases and include all gases found in vehicle emissions. Reducing the emissions of carbon dioxide (CO2) has become an urgent necessity around the world, and many countries have imposed limits on their CO2 output. Using biofuels in automotive engines is an effective way of reducing greenhouse gas emissions. The CO2 emissions emitted from the combustion of biofuels are absorbed as trees and plants grow. Biofuels can be used either as pure fuels or blended with conventional fuels. Most research has declared that the most effective way to reduce greenhouse emissions is the use of various biofuels. Therefore, it is essential to assess the outcomes of research regarding alternate fuels or fuel additives to determine their proper utilization. Using of diesel engines can also help reduce CO2 emissions as they emit less CO2 emissions than gasoline engines. This review study investigates the effects the using of dimethyl ether on CO2 emissions in diesel engines. The results showed that CO2 emissions decrease when using the pure DME and DME blends with diesel and LPG fuels due to the oxygen content and the lower carbon to hydrogen ratio of DME. The decrements in CO2 emissions for pure DME, diesel–DME blends and LPG–DME blend are about 5.2–18.3%, 3–41.6% and 10.6–16.4%, respectively.

In cases where the dependent variable is categorical and ordinal, the Ordinal Logistic Regression Model is used for model estimation. In order to predict the Ordinal Logistic Regression Model, it must provide the parallel lines assumption. In the study, the happiness levels of divorced individuals were estimated with the ordinal logistic regression model. The data set used in the analysis was obtained from the Life Satisfaction Survey implemented by the Turkish Statistical Institute in 2020. Brant's Wald Test and Likelihood Ratio Test were applied for the parallel lines assumption and the null hypothesis could not be rejected. In this context, the model ordinal logistic regression model was estimated. The statistically significant gender variable shows that divorced women are happier than divorced men. It has been determined that success and job variables tend to decrease happiness levels in divorced individuals compared to other factors. In general, when the education level is examined, it is seen that the level of happiness of divorced individuals increases as the education level increases. It has been concluded that divorced individuals who are satisfied with their health, education, income and social life are happier than divorced individuals who are not satisfied with their health, education, income and social life. It has been determined that divorced individuals are registered with the Social Security Institution and their happiness levels are higher than those who are not registered.

Herein, carbon nanotube supported bimetallic catalysts (PtBi/CNT) are synthesized at various metals weight loadings by NaBH4 reduction method. The surface morphology and crystal structure of the catalysts are investigated via X-ray diffraction (XRD) and electron microscopy with energy dispersive X-ray (SEM-EDX) advance surface methods. According to XRD results, the crystal size of PtBi(90:10)/CNT catalyst is determined as 4.66 nm. Cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), and chronoamperometry (CA) electrochemical techniques are used to determine 1-propanol electrooxidation activities of the catalysts. The highest specific activity and mass activity are obtained with PtBi(90:10)/CNT catalyst as 5.663 mA/cm2 and 447.21 mA/mg Pt, respectively. However, it is concluded that PtBi(90:10)/CNT catalyst can be used as an anode catalyst in 1-propanol electrooxidation with long-term stability and low resistance.

A new phthalocyanine 1 was synthesized, containing 2-aminophenoxy groups as tetra from its peripheral positions and indium(III) acetate in its cavity. The structure of 1 was illuminated by elemental analysis, FTIR, UV-vis and fluorescence spectroscopy techniques It is well soluble in solvents such as polar and nonpolar solvent types. Its aggregation properties have been studied both in the solvents mentioned above and in Dimethyl sulfoxide at different concentrations. Its aggregation properties were examined in these solvents as well as in Dimethyl sulfoxide at certain different concentration ranges. With these determined spectroscopic properties, it can be a candidate for use in various technological applications.

In this paper, the influence of the Static Var Compensators (SVC) on distance relay protection when connected to the sub-transmission network of the Nigerian 132-kV grid system is investigated. This is carried out by monitoring the error margin of the fault locator associated with the distance protective relays when SVC is connected to the transmission line. The location of the SVC is selected such that there is a common primary source of power to the sub-transmission network (Ikorodu-Sagamu 132-kV transmission line) and the SVC, which is a shunt-connected device located on Ikorodu 132-kV bus. The fault is simulated at 33.6 km and 60.4 km respectively from the Ikorodu 132-kV sub-station in Matlab/ Simulink model and the simulation results obtained are used to investigate the influence of SVC on distance protective relay when connected to the 132-kV power transmission line. The results of the line faults are obtained with respect to earth for both zones one (1) and two (2) when SVC is connected and disconnected for all shunt type of faults. Thus, indicating under-reach and over-reach characteristics of the distance relay, when the SVC is connected and disconnected, while the transmission line protection showed no under-reach or over-reach characteristics for line-to-line fault for both zones with the SVC connected or disconnected. The results of this study show that though SVCs improve the quality of power to consumers, there is a tendency for under-reach and over-reach characteristics of the protective relay to be displayed when the relays are disconnected and connected, which introduces error margins to the fault locator in distance protective relays. Therefore, more detailed dynamic simulations are recommended for reducing the error margin.

Feature extraction layers like Local Binary Patterns (LBP) transform can be very useful for improving the accuracy of machine learning and deep learning models depending on the problem type. Direct implementations of such layers in Python may result in long running times, and training a computer vision model may be delayed significantly. For this purpose, TensorFlow framework enables developing accelerated custom operations based on the existing operations which already have support for accelerated hardware such as multicore CPU and GPU. In this study, LBP transform which is used for feature extraction in various applications, was implemented based on TensorFlow operations. The evaluations were done using both standard Python operations and TensorFlow library for performance comparisons. The experiments were realized using images in various dimensions and various batch sizes. Numerical results show that algorithm based on TensorFlow operations provides good acceleration rates over Python runs. The implementation of LBP can be used for the accelerated computing for various feature extraction purposes including machine learning as well as in deep learning applications.