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Abstract The most cost-effective data collection method is electroencephalography (EEG), which obtains meaningful information about the brain. Therefore, EEG signal processing is crucial for neuroscience and machine learning (ML). Therefore, a new EEG stress dataset has been collected, and an explainable feature engineering (XFE) model has been proposed using the Directed Lobish (DLob) symbolic language. The first phase of this research is the data collection phase, and an EEG stress dataset was gathered from 310 participants. This collected stress dataset contains two classes: (i) stress and (ii) control. An XFE model has been presented to detect stress automatically. The presented XFE model has four main phases, and these are (i) channel transformer and quadruple transition pattern (QuadTPat)-based feature generation, (ii) feature selection deploying cumulative weighted neighborhood component analysis (CWNCA), (iii) explainable results creation with DLob and (iv) classification with t algorithm-based k-nearest neighbors (tkNN) classifier. The proposed XFE model generates a DLob string, and the explainable results were obtained using this string. Moreover, the presented XFE model attained 92.95% and 73.63% classification accuracy, deploying 10-fold and leave-one subject-out (LOSO) cross-validations (CV). According to the classification performances, the recommended QuadTPat-based XFE is a good model for EEG signal classification. Also, the presented QuadTPat-based XFE model is a good model for explainable artificial intelligence (XAI) since TTPat-based XFE is cooperating with the DLob.

Abstract The most cost-effective data collection method is electroencephalography (EEG) to obtain meaningful information about the brain. Therefore, EEG signal processing is very important for neuroscience and machine learning (ML). The primary objective of this research is to detect neonatal seizures and explain these seizures using the new version of Directed Lobish. This research uses a publicly available neonatal EEG signal dataset to get comparative results. In order to classify these EEG signals, an explainable feature engineering (EFE) model has been proposed. In this EFE model, there are four essential phases and these phases: (i) automaton and transformer-based feature extraction, (ii) feature selection deploying cumulative weight-based neighborhood component analysis (CWNCA), (iii) the Directed Lobish (DLob) and Causal Connectome Theory (CCT)-based explainable result generation and (iv) classification deploying t algorithm-based support vector machine (tSVM). In the first phase, we have used a channel transformer to get channel numbers and these values have been divided into three levels and these levels are named (1) high, (2) medium and (3) low. By utilizing these levels, we have created an automaton and this automaton has three nodes (each node defines each level). In the feature extraction phase, transition tables of these nodes has been extracted. Therefore, the proposed feature extraction function is termed Triple Nodes Automaton-based Transition table Pattern (TATPat). The used EEG signal dataset contains 19 channels and there are 9 (= 32) connection in the defined automaton. Thus, the presented TATPat extracts 3249 (= 19 × 19 × 9) features from each EEG segment. To choose the most informative features of these 3249 features, a new feature selector which is CWNCA has been applied. By cooperating findings of this feature selector and the presented DLob, the explainable results have been obtained. The last phase is the classification phase and to get high classification performance from this phase, an ensemble classifier (tSVM) has been presented and the classification results have been obtained using two validation techniques which are 10-fold cross-validation (CV) and leave-one subject-out (LOSO) CV. The proposed EFE model generates a DLob string and by using this string, the explainable results have been obtained. Moreover, the presented EFE model attained 99.15% and 76.37% classification accuracy deploying 10-fold and LOSO CVs respectively. According to the classification performances, the recommended TATPat-based EFE is a good model at EEG signal classification. Also, the presented TATPat-based EFE model is a good model for explainable artificial intelligence (XAI) since TTPat-based EFE is cooperating by the DLob.

The aim of this research is to determine whether the use of augmented reality applications in the teaching of the subject of ''Geometric objects'' in the sixth grade mathematics class has an effect on the development of students' geometric thinking levels and spatial ability. The research was conducted using a pre experimental design, one of the quantitative research methods. The research was carried out with 15 sixth grade students studying in a state secondary school in Aziziye district of Erzurum province in the spring semester of the 2021/2022 academic year, for 15 class hours which each of the lessons consist of 40 minutes. While collecting data in the research, van Hiele Geometric Thinking Test, Middle Grades Mathematics Projects (MGMP) Spatial Ability Test and the Geometric Objects Achievement Test were used. Statistical package program was used in the analysis of the data. As a result of the analysis of the data obtained from the van Hiele Geometric Thinking Test, the MGMP Spatial Ability Test and the Geometric Objects Achievement Test, it was determined that there was a statistically significant difference between the pretest and the posttest in favor of the posttest. It concluded that teaching geometry with augmented reality applications had a positive effect on the development of students' geometric thinking levels and spatial ability, and in addition, it had a statistically positive contribution to their academic achievement. Therefore, in line with these results, it can be said that augmented reality-based teaching applications offer new opportunities for learning- teaching processes.

Low level of health literacy causes a decrease in quality of life and social inequalities. Adequate health literacy levels of pre-service teachers will not only reduce the risk of disease, but also will contribute to development of a healthy lifestyle for societies. In the study, it is aimed to determine the health literacy levels of pre-service teachers, and to reveal how it changes in terms of various socio-demographic characteristics. The study group of the research consists of 482 pre-service teachers studying at the education faculties of state universities. The 'Turkey Health Literacy Scale-32' was used to determine the health literacy levels of the participants. In the analysis of the data parametric tests were used, since there were no outliers, and the normality assumptions were met. The results show that two-thirds (68%) of the research group have inadequate and limited health literacy. When health literacy index scores examined in terms of the variables discussed in the study, it is understood that the level of health literacy remains at a limited level. It is thought that making arrangements to improve health literacy in the curriculum of teacher training programs will directly contribute to a healthier life for both individuals and societies.

Abstract Objective Interstitial lung diseases (ILDs) are diverse pulmonary disorders marked by diffuse lung inflammation and fibrosis. The variability in characteristics and treatment approaches complicates diagnosis and management. In advanced cases requiring transplantation, determining indications and selecting suitable candidates presents additional challenges. Methods Of all patients with non-IPF ILD between December 2016 to December 2022 were analyzed retrospectively. Patients were categorized into two groups: transplanted patients and deceased patients on the waiting list. Clinical data and survival outcomes were compared between groups. Results Of the 43 patients, 20 underwent lung transplantation while 23 died awaiting transplantation. Waiting list mortality was 53.4%, with median waiting times similar between groups (3 months for transplant patients and 6 months for those on the waiting list). There were no significant differences between groups in age, gender, height, BMI, 6-minute walk test (6MWT), or forced vital capacity (FVC). The prevalence of pulmonary hypertension (PH) was 76.7% in right heart catheterizations, similar in both groups. One single and 19 bilateral lung transplants were performed. Overall, 13 of the 20 patients survived to discharge from the hospital. One-year mortality was 7/20 (35%). The median follow-up was 34 months, with a 1-year conditional survival of 90.9% at 3 years and 70.7% at 5 years. Conclusions This study underscores the importance of further research into non-IPF ILDs. Lung transplantation remains a viable option that can significantly enhance both the quality and longevity of life for patients with advanced ILD.

Abstract Background This study aimed to evaluate the outcomes of patients diagnosed with stage IB2/IIA2 cervical squamous cell carcinoma who underwent neoadjuvant chemotherapy (NACT) prior to radical hysterectomy compared to those who did not receive NACT before surgery. Materials and methods This is a multicenter study including data of 6 gynecological oncology departments. The study is approved from one of the institution’s local ethics committee. Patients were stratified into two cohorts based on the receipt of NACT preceding their surgical intervention. Clinico-pathological factors and progression-free survival were analyzed. Results Totally 87 patients were included. Lymphovascular space invasion (LVSI) was observed as 40% in the group receiving NACT, while it was 66.1% in the group not receiving NACT (p = 0.036). Deep stromal invasion (> 50%) was 56% in the group receiving NACT and 84.8% in the group not receiving NACT (p = 0.001). In the univariate analysis, application of NACT is statistically significant among the factors that would be associated with disease-free survival. Consequently, a multivariate analysis was conducted for progression-free survival, incorporating factors such as the depth of stromal invasion, the presence of LVSI, and the administration of NACT. Of these, only the administration of NACT emerged as an independent predictor associated with decreased progression-free survival. (RR:5.88; 95% CI: 1.63–21.25; p = 0.07). Conclusions NACT shouldn’t be used routinely in patients with stage IB2/IIA2 cervical cancer before radical surgery. Presented as oral presentation at National Congress of Gynaecological Oncology & National Congress of Cervical Pathologies and Colposcopy (2022/ TURKEY).

The aim of this study, which was in a qualitative research design, is to reveal the perceptions of education administrators working in higher education institutions about the concept of sustainability through metaphors. The phenomenological model, which is based on the individual's describing a conscious experience of a phenomenon, and in which the researcher tries to reach the life world created by each participant's own subjective experiences, was used. After data collection, the metaphors produced by the education administrators regarding the concept of sustainability of education administrators were analyzed and categorized. As a result of the research, the meanings attributed to the concept of sustainability by education administrators working in higher education institutions were revealed.

The main aim of the present study is to investigate the emotional intelligence of faculty members based on their career stage and gender. In the study, a mixed method was employed to determine the emotional intelligence of faculty members and to reach generalizable findings. The convenience sampling method was used in the quantitative study, while the quota sampling method was used in the qualitative section. The quantitative section sample included faculty members in various stages in their careers. In the qualitative dimension, the sample included faculty members who participated in the quantitative application. The quantitative study findings demonstrated that there were significant differences between the emotional intelligence competencies of female and male faculty members in the workplace based on the empathy and self-awareness dimensions. The qualitative study findings revealed that there were differences between the emotional intelligence dimensions, especially based on the career stage and gender. Recommendations are presented based on the study findings.

Methyl alcohol consumption results in serious poisoning symptoms and even causes death, so it is not used in legal alcoholic beverages. We aimed to examine deaths due to methyl alcohol intoxication in our region through forensic autopsies performed in İzmir-Turkey to compare these data with international studies. A total of 13,701 autopsies performed between 2016-2021 were scanned retrospectively. 214 cases in which the exact cause of death was methanol intoxication were included in the study. All the cases were analyzed in terms of age, gender, date of death, place of death, length of hospitalization, methanol levels detected in blood and the vitreous humor, ethanol presence in blood, and pathological findings. The ratio of autopsies of the exact cause of death of methanol poisoning to total autopsies is 1.56%. Of these cases, 94.9% were male and 5.1% were female. The age range was 25–80 years, with a mean age of 53.4. According to the age distributions, the highest rate was 39.3% in the sixth decade. The highest number of cases was 129 (60.3%) in 2020. In the analysis of blood methanol levels, the concentration was found to be in the range of 0-642 mg/dl and the mean was 178 mg/dl. During the COVID-19 pandemic, there has been a significant increase in methanol poisoning cases associated with the economic and social problems experienced in Turkey. Our study showed that methanol poisoning deaths are especially common in men aged 50-60 years so this group is at higher risk of methanol poisoning. [Med-Science 2023; 12(4.000): 1324-30]

Electroencephalogram (EEG) signals contain information about the brain’s state as they reflect the brain’s functioning. However, the manual interpretation of EEG signals is tedious and time-consuming. Therefore, automatic EEG translation models need to be proposed using machine learning methods. In this study, we proposed an innovative method to achieve high classification performance with explainable results. We introduce channel-based transformation, a channel pattern (ChannelPat), the t algorithm, and Lobish (a symbolic language). By using channel-based transformation, EEG signals were encoded using the index of the channels. The proposed ChannelPat feature extractor encoded the transition between two channels and served as a histogram-based feature extractor. An iterative neighborhood component analysis (INCA) feature selector was employed to select the most informative features, and the selected features were fed into a new ensemble k-nearest neighbor (tkNN) classifier. To evaluate the classification capability of the proposed channel-based EEG language detection model, a new EEG language dataset comprising Arabic and Turkish was collected. Additionally, Lobish was introduced to obtain explainable outcomes from the proposed EEG language detection model. The proposed channel-based feature engineering model was applied to the collected EEG language dataset, achieving a classification accuracy of 98.59%. Lobish extracted meaningful information from the cortex of the brain for language detection.