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The study aimed to support the resolution of problems experienced due to preschool children not adjusting to school at the start of first schooling. Thus, problems leading to school rejection and many other probable issues will be prevented. The experimental study about orientation problems occurring in students newly starting preschool used a semi-experimental method with implementations to ensure school adjustment. The study included 53 students with ages between 60-72 months with frequent school absenteeism due to a variety of reasons. Parents of selected students were given information and the general lines of the Designed Adjustment Program (DAP) were explained. The study applied to the students who could not adapt to the school and have experienced school refusal included a preparation phase for a maximum of 1 week. Afterwards, it covers a minimum of 8 and a maximum of 12 days of application. Accordingly, all students in the experimental group to whom DAP implemented adjusted to school, while 66.7% of students in the placebo group and 61.5% of students in the control group were identified to adjust to school. In conclusion, it was identified that the parental training about duties falling to parents during school adjustment provided positive results, individual studies actively including parents, school and teachers were more effective, peer acceptance was important for adjustment to school, there was a need to use social skill acquirements, and girls were more successful at adjustment to school compared to boys.

A transitional object is the selection and binding to an object that reminds the child of the mother and helps deal with separation anxiety in situations where the child is separated from the mother. In reality, many children are observed to have transitional objects and no problems occur. In this case report, the transitional object became pathological. An only child, the boy was 3.5 years and wished to wear the pyjamas chosen as transitional object in all areas of life. According to anamnesis from the family, there was no socio-economic problem experienced. Additionally, the child's developmental curves were normal and there was no physiological health problem. The child experienced problems in home and school life and this situation negatively affected the mother's home and professional life, so the case was dealt with. The study applied a strategy developed based on a behavioral approach combined with an object relation approach. At the end of the four-week program completed with the parents and child's teacher together, the problem was solved and follow-up one month later identified that this had not changed.

The aim of the study is to develop a belonging to the university scale (BUS) in order to determine the level of fulfillment of the need to belong among university students at the higher education institutions they attend. The population of the investigation includes university students studying at the campus of Ordu University. A 5 point Likert-type scale was created in the study. The SPSS 15 and LISREL-8.15 programs were used in the statistical analysis performed during development of the scale. Content validity and construct validity techniques were used for validity analysis. The internal consistency checks of belonging to the university scale were completed. The research data is given as mean and percent, and t-test and one-way variance analysis were conducted in order to determine the differences between the groups and the values of belonging to the university. The internal consistency Cronbach a reliability coefficient values of the scale and the test-retest reliability of the test results were satisfactory. Exploratory and confirmatory factor analyses were performed for the construct validity of the scale and three dimensions (Motivation, Expectations and Identification) were identified. The level of belonging of university students to the university they attend was examined with the scale with the validity and reliability tested and suggestions are presented in accordance with the obtained results.

Aim: Millions of people of all ages around the world are overweight or obese. Although many try dieting to lose their excess weight, the success rate is 20%, and only 2% are able to control their weight. This process becomes a vicious cycle of constantly gaining and losing weight, which is called yo-yo syndrome. Previous studies have found that obesity awareness was low in samples of university students. The present study aimed to determine the reasons that obese university students develop yo-yo syndrome by analyzing their dietary histories. Materials and Methods: This qualitative study applied a phenomenological research method. The study population consisted of nine students in various faculties at Ordu University. Semi-structured interviews were conducted, and demographic information, such as age and gender, was collected to determine the number of diets that were started and stopped. Data were collected from the Faculty of Education. Results: The following themes emerged from the data analysis: defense mechanisms in the categories of rationalization, denial, and projection; boredom in the category of boredom; health in the categories of fear of disease and death anxiety; social-physical anxiety in the categories of bodily discontent and mockery; and social anxiety in the categories of cultural pressure, peer relationships, and close relationships. Conclusion: The reasons that the university students in the study sample developed yo-yo syndrome were identified according to the categories and themes that emerged from the data analysis. Based on these findings, it has been suggested that psycho-education, psychiatric treatment, and follow-ups would be beneficial in treating yo-yo syndrome. [ABSTRACT FROM AUTHOR]

Accurate instantaneous electricity peak load prediction is crucial for efficient capacity planning and cost-effective electricity network establishment. This paper aims to enhance the accuracy of instantaneous peak load forecasting by employing models incorporating various optimization and machine learning (ML) methods. This study examines the impact of independent inputs on peak load estimation through various combinations and subsets using multilinear regression (MLR) equations. This research utilizes input data from 1980 to 2020, including import and export data, population, and gross domestic product (GDP), to forecast the instantaneous electricity peak load as the output value. The effectiveness of these techniques is evaluated based on error metrics, including mean absolute error (MAE), mean square error (MSE), mean absolute percentage error (MAPE), root mean square error (RMSE), and R2. The comparison extends to popular optimization methods, such as particle swarm optimization (PSO), and the newest method in the field, including dandelion optimizer (DO) and gold rush optimizer (GRO). This comparison is made against conventional machine learning methods, such as support vector regression (SVR) and artificial neural network (ANN), in terms of their prediction accuracy. The findings indicate that the ANN and GRO approaches produce the least statistical errors. Furthermore, the correlation matrix indicates a robust positive linear correlation between GDP and instantaneous peak load. The proposed model demonstrates strong predictive capabilities for estimating peak load, with ANN and GRO performing exceptionally well compared to other methods. [ABSTRACT FROM AUTHOR]

Aim: COVID is a life-threatening pandemic disease that can lead to panic-attack disorder, anxiety, mourning, loss and depression. However, it is thought that it may negatively affect their lifestyle, personal hygiene and perception of anxiety. In this direction, the aim of the study is to examine the lifestyle, personal hygiene and anxiety perceptions of individuals after the COVID-19 pandemic. Materials and Methods: In the study, a 12-item questionnaire was applied to determine the perceptions of individuals about their lifestyle, personal hygiene and concerns after the epidemic. The questionnaire was applied via the internet and the data collected is limited to the responses of participants. Data was collected from a total of 2003 social media users from different provinces using convenience sampling. Results: In the study, it was thought that the participants would significantly change their lifestyles after COVID-19. Accordingly, while online shopping has increased relatively, behavioral changes regarding active living spaces have also developed. Conclusion: According to the data in the study, significant changes are expected in the lifestyles of societies after the COVID-19 pandemic. New lifestyles, marked by moving away from crowds, will transform consumer styles. In this direction, it seems inevitable to create new policies for various social areas such as entertainment world, tourism, food and beverage sector, transportation, architecture and shopping. [ABSTRACT FROM AUTHOR]

Medium Neural Networks (MNN), Whale Optimization Algorithm (WAO), and Support Vector Machine (SVM) methods are frequently used in the literature for estimating electricity demand. The objective of this study was to make an estimation of the electricity demand for Turkey's mainland with the use of mixed methods of MNN, WAO, and SVM. Imports, exports, gross domestic product (GDP), and population data are used based on input data from 1980 to 2019 for mainland Turkey, and the electricity demands up to 2040 are forecasted as an output value. The performance of methods was analyzed using statistical error metrics Root Mean Square Error (RMSE), Mean Absolute Error (MAE), R-squared, and Mean Square Error (MSE). The correlation matrix was utilized to demonstrate the relationship between the actual data and calculated values and the relationship between dependent and independent variables. The p-value and confidence interval analysis of statistical methods was performed to determine which method was more effective. It was observed that the minimum RMSE, MSE, and MAE statistical errors are 5.325 × 10−14, 28.35 × 10−28, and 2.5 × 10−14, respectively. The MNN methods showed the strongest correlation between electricity demand forecasting and real data among all the applications tested. [ABSTRACT FROM AUTHOR]

This study reviews a selection of approaches that have used Artificial Neural Networks (ANN), Particle Swarm Optimization (PSO), and Multi Linear Regression (MLR) to forecast electricity demand for Gokceada Island. Artificial Neural Networks, Particle Swarm Optimization, and Linear Regression methods are frequently used in the literature. Imports, exports, car numbers, and tourist-passenger numbers are used as based on input values from 2014 to 2020 for Gokceada Island, and the electricity energy demands up to 2040 are estimated as an output value. The results obtained were analyzed using statistical error metrics such as R2, MSE, RMSE, and MAE. The confidence interval analysis of the methods was performed. The correlation matrix is used to show the relationship between the actual value and method outputs and the relationship between independent and dependent variables. It was observed that ANN yields the highest confidence interval of 95% among the method utilized, and the statistical error metrics have the highest correlation for ANN methods between electricity demand output and actual data. [ABSTRACT FROM AUTHOR]

Parasitic infections especially affect those children in critical phases of growing and development. They can lead to malnutrition, malabsorption, mental and social adaptation problems, and critical illnesses that hinder productivity. This is an important health problem in Turkey, and intestinal parasites are quite frequently found in boarding school and care centers for children. The aim of this study was to discover and treat the parasitological agents found in the students of the Primary School for Deaf Students. A total of 117 fecal specimens and material from the perianal region (using the cellophane tape method) were collected from deaf students and examined during 2003-2004 school season. Also the students were scanned for adult head lice and their eggs. As a result, 53 (45.3%) children with intestinal parasites and 6 (5.1%) with head lice were detected in the study. The patients were treated and informed about means of protection. The same children were examined again after six months: Only 24 (20.5%) children had intestinal parasites and 2 (1.7%), head lice. Thus, the decrease in parasitic infections was found to be significant (qui-square test in dependent samples P < 0.05). Therefore it was concluded that the schools should be scanned for intestinal parasites and head lice, and both treatment and training in the subject should be given the necessary importance.