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In this paper, the flux error-related weighting factor (WF) of the predictive torque control (PTC) strategy for induction motor (IM) control is optimised by a standard genetic algorithm (SGA) through speed errors only and multi-objective genetic algorithm (MOGA) through torque and flux errors. This paper compares the performances of both optimisation methods. Compared to MOGA, SGA offers a straightforward way to select WF and does not need a decision-making method to choose a final solution. But MOGA considers the given problem in a multi-objective way and directly optimises the control objectives of the PTC strate-gy. Comparisons are made over the flux and torque ripples, total harmonic distortion of stator phase current, and average switching frequency for different operating conditions. Simulation results show that both methods choose a close WF value. Consequently, SGA stands out in the optimisation of the PTC strategy with its simple structure.

Today, a clear trend in electrification process has emerged in all areas to cope with carbon emissions. For this purpose, the widespread use of electric cars and wind energy conversion systems has increased the attention and importance of electric machines. To overcome limitations in mature control techniques, model predictive control (MPC) strategies have been proposed. Of these strategies, predictive torque control (PTC) has been well accepted in the control of electric machines. However, it suffers from the selection of weighting factors in the cost function. In this paper, the weighting factor associated with the flux error term is optimised by the non-dominated sorting genetic algorithm (NSGA-II) algorithm through torque and flux errors. The NSGA-II algorithm generates a set of optimal solutions called Pareto front solutions, and a possible solution must be selected from among the Pareto front solutions for use in the PTC strategy. Unlike the current literature, three decision-making methods are applied to the Pareto front solutions and the weighting factors selected by each method are tested under different operating conditions in terms of torque ripples, flux ripples, cur-rent harmonics and average switching frequencies. Finally, a decision-making method is recommended.

Purpose: Acute kidney injury (AKI) is a severe kidney disease carrying high morbidity and mortality. An ischemic process, at the cellular level, has been detected prior to the full-blown AKI. An elevated ischemic modified albumin (IMA) was also found to be increased fast at several minutes following an ischemic process in the body. In this connection, we have investigated, in advance, the changes of IMA concentrations in patients with possible AKI. Methods: IMA and other biochemical and haematological parameters were measured in sera of thirty nine patients with AKI and of thirty eight healthy controls. AKI is defined by an increase in serum creatinine by ≥ 0.3 mg/dl in 48 hours or an increase by ≥ 1.5-fold from a known or assumed baseline. The results in the two groups were compared. Results: IMA, creatinine, blood urea nitrogen, white blood cell, neutrophil, neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR) and mean platelet volume were found to be higher in patients with AKI than in healthy controls. In contrast, total protein, albumin, lymphocyte, and haemoglobin were lower in patients with AKI than in healthy controls. No significant difference was recorded in platelet counts between the two groups. Conclusion: Our results indicate that increased levels of NLR and PLR play a central role in a systemic inflammation in AKI. Monitoring serum IMA could be a useful tool in the assessment of AKI.

Background: Due to the lack of diagnostic efficiency of serum creatinine in acute kidney injury (AKI), there is a pressing need to develop novel diagnostic markers. Therefore, in this study, we evaluated myo-inositol oxygenase (M IOX), neutrophil gelatinase-associated lipocalin (NGAL) and cystatin C in terms of their applicability in the diagnosis of AKI. Methods: We enrolled a total of 39 AKI patients and 38 healthy controls in the study. We compared the levels of serum MIOX, NGAL and cystatin C between the two groups. Results: We found that the concentrations of serum creatinine, blood-urea nitrogen, M IOX and cystatin C were higher in the AKI group. According to the receiver operating characteristic analysis, the area under the curve (AUC) values were 0.694 (95% CI 0.579-0.794) for M IOX and 0.976 (95% CI; 0.912-0.997) for cystatin C. For MIOX, when the cut-off concentration was set to 77.3 pg/mL, the diagnostic sensitivity and specificity were found to be 53.8% (95% CI; 37.2-69.9) and 81.5 (95% CI; 65.7-92.3), respectively. For cystatin C, at the cut-off value of 14 mg/L, the diagnostic sensitivity and specificity were 94.8% (95% CI; 82.7-99.4) and 94.7 % (95% CI 82.3-99.4), respectively. Conclusions: The measurement of serum MIOX and cystatin C levels is valuable for the diagnosis of AKI. Further research is needed for the evaluation of the potential use of MIOX as a kidney-specific enzyme in the early diagnosis of AKI.

Mustafa Oran,1 Cuneyt Unsal,2 Yakup Albayrak,2 Feti Tulubas,3 Keriman Oguz,4 Okan Avci,1 Nilda Turgut,4 Recep Alp,4 Ahmet Gurel3 1Department of Internal Medicine, 2Department of Psychiatry, 3Department of Biochemistry, 4Department of Neurology, Namik Kemal University, Faculty of Medicine, Tekirdağ, Turkey Background and aim: Restless legs syndrome (RLS) is a distressing sleep disorder that occurs worldwide. Although there have been recent developments in understanding the pathophysiology of RLS, the exact mechanism of the disease has not been well elucidated. An increased prevalence of neurologic and psychiatric diseases involving dopaminergic dysfunction in vitamin D-deficient patients led us to hypothesize that vitamin D deficiency might result in dopaminergic dysfunction and consequently, the development of RLS (in which dopaminergic dysfunction plays a pivotal role). Thus, the aim of this study was to evaluate the relationship between vitamin D deficiency and RLS. Methods: One hundred and fifty-five consecutive patients, 18–65 years of age, who were admitted to the Department of Internal Medicine with musculoskeletal symptoms and who subsequently underwent neurological and electromyography (EMG) examination by the same senior neurologist, were included in this study. The patients were divided into two groups according to serum 25-hydroxyvitamin D (25(OH)D) (a vitamin D metabolite used as a measure of vitamin D status) level: 36 patients with serum 25(OH)D levels ≥20 ng/mL comprised the normal vitamin D group, and 119 patients with serum 25(OH)D levels

Assessing students' online learning readiness is important since numerous countries have started online learning at all education levels during the COVID-19 pandemic in the 21st century. By taking students' online learning readiness level into account, it will be easier to establish on-target online learning environments. Although there are a number of online learning readiness scales available aiming at higher-education students in the Turkish setting, there is no scale available specifically for high-school students. This study, therefore, aims to develop a valid and reliable scale to identify the levels of online learning readiness for high school students in Türkiye. In order to develop an Online Learning Readiness Scale for high school students, a mixed-method exploratory sequential design was employed in this study. The first sample consisted of 916 students and the second sample consisted of 323 students who had previously experienced an online learning environment. The data were analyzed through exploratory factor analysis and confirmatory factor analysis. Validity and reliability evidences were also provided. The final version of the scale consisted of a total of 16 items in three dimensions; namely, computer self-efficacy, internet self-efficacy, and self-learning and explained 65.76% of the variance. The results of the study indicate that the Online Learning Readiness Scale (OLRS) developed in this particular study is a reliable and valid measurement tool in the assessment of online learning readiness levels of high school students in Türkiye and is expected to guide researchers and practitioners who focus on assessing high school students' online learning readiness levels.

The aim of this study is to investigate the types of teacher and peer interventions to test the validity of modelling along with the modelers' reactions to these interventions. A teaching experiment was employed in the study. Forty-five pre-service secondary school mathematics teachers participated in the research. As a data collection tool, a worksheet was prepared by taking the monthly average temperature values of the last five years from the Meteorology Directorate of the province in which the study was conducted. At the end of the mathematical modelling process, teacher and peer interventions were carried out. The types of teacher and peer interventions were categorized. The reasons for intervening in the modelling process by both the teacher and peers were insufficient explanations and student error. In addition, the teacher intervened to advance the process. The teacher intervened for diagnosis, advice and feedback, and the peers intervened for diagnosis and feedback in this process. While teacher intervention occurred at the stage of mathematical model and results, peer intervention occurred at the stage of testing the real model and result. The participants returned to revise their real model, mathematical model and results. In the modeling process, while peer support may be sufficient to test the validity of the real model and real results, teacher support is required to test the validity of the mathematical model and its results.

The Colorado Learning Attitudes about Science Survey (CLASS) is an instrument to measure student beliefs about physics and learning physics. In this research, Turkish adaptation and psychometric evaluation of the CLASS is discussed. In the first stage, the translation process, which included examination of six experts (four experts in physics education and two experts in English and Turkish languages) for content validity and 13 student interviews for face validity, was described. In the second stage, exploratory (EFA) and confirmatory (CFA) factor analysis results obtained from 1391 freshman students were discussed for construct validity. The EFA yielded three factors that consisted of 20 items, which explained 39.61 % of the total variance. These factors were named as: "Problem Solving Effort, Conceptual Understanding, and Personal Interest and Real-World Connection." Based on the CFA results, the three-factor 20-item instrument showed acceptable fit statistics. Compared to the original CLASS, the proposed version with 20-item model was shorter, easier to administer and easier to score, valid and reliable survey, so feasible to use. The overlapping of the constructs (i.e., violation of the unidimensionality) in the original CLASS was a problem, while in the proposed model none of the items were included in more than one construct.

This study aims to compare two different learning environments designed online in terms of their contributions to the development of mathematical modelling competencies. In the research, which was designed with the triangulation method, among the mixed research methods in which the qualitative and quantitative research methods are used together. The participants, determined with the purposeful sampling technique, consisted of 53 fourth class students who were studying at the mathematical teaching department of a faculty of education. In the research, while the quantitative data were analyzed with the paired samples t-test and Wilcoxon test, the qualitative data were analyzed with the content analysis. As a result of the analyses, while a significant difference was found in favour of the post-test between the modelling test questions of the pre-service teachers who attended the distance education in groups, the modelling test results of the pre-service teachers who attended individually did not differ. In addition, while it was observed that the participants who attended individually had difficulty in expressing themselves more throughout the process, it was determined that those who worked with groups tried to verify the results they reached more during the modelling process compared with those who participated individually.