The purpose of this research is to understand the relationship between green business ethics, green finance, and sustainable business performance, and to evaluate the role of corporate social responsibility (CSR) in this relationship. The impact of the damage inflicted on nature’s functioning order is being felt much more strongly today. In light of these realities, companies must emphasize sustainability principles not just out of financial concerns but as a result of corporate social responsibility. In this context, focusing on the role of corporate social responsibility in sustainable business performance is the main goal of this research. Quantitative research methods, specifically the cross-sectional survey method, were employed for data collection and analysis. For this purpose, a convenience sampling method was used to select 427 white-collar employees working in industries operating in Türkiye as the sample for this study. The data collected through surveys were analyzed using the AMOS 24 statistical program. The findings underscore that green business ethics and green finance have a significant impact on corporate social responsibility and sustainable business performance. Additionally, it was determined that corporate social responsibility plays an intermediary role in shaping sustainable business performance. These findings are expected to provide an important foundation that can guide both employees and managers in developing awareness about green policies and sustainability, emphasizing the importance of green policies in working life.

Long waiting times in polyclinics are a critical factor affecting patient satisfaction and the efficient use of healthcare personnel and resources. This study applied machine learning (ML) algorithms to predict and reduce patient waiting times in a dental clinic in Türkiye. The daily data collected from the clinic included variables such as patient satisfaction, appointment patients, Walk-in patients, number of doctors and nurses, and dental technicians on duty. Six ML algorithms were tested: Decision Trees (DT), Linear Regression (LR), Support Vector Machines (SVM), Gaussian Process Regression (GPR), Kernel Regression (KR), and Neural Networks (NN). Among these, the GPR model achieved the best performance, accurately predicting patient waiting times with an R2 value of 0.936 and RMSE of 0.075. This study highlights the potential of ML methods to enhance operational efficiency in healthcare management.

Providing electricity needs from renewable energy sources is an important issue in the energy policies of countries. Especially changes in energy usage rates make it necessary to use renewable energy resources to be sustainable. The electricity usage rate must be estimated accurately to make reliable decisions in strategic planning and future investments in renewable energy. This study aims to accurately estimate the renewable energy production rate to meet Türkiye’s electricity needs from renewable energy sources. For this purpose, well-known Machine Learning (ML) algorithms such as Random Forest (RF), Adaptive Boosting (AB), and Gradient Boosting (GB) were utilized. In obtaining forecast data, 15 variables were considered under the oil resources, environmental parameters, and economic factors which are the main parameters affecting renewable energy usage rates. The RF algorithm performed best with the lowest mean absolute percentage error (MAPE, 0.084%), mean absolute error (MAE, 0.035), root mean square error (RMSE, 0.063), and mean squared error (MSE, 0.004) values in the test dataset. The R² value of this model is 0.996% and the MAPE value is calculated lower than 10%. The AB model, on the other hand, has the highest error values in the test data set, but still provides an acceptable prediction accuracy. The R² value was 0.792% and the MAPE value (0.371%) of this model was calculated to be in the range of 20% < MAPE≤50%. This study, with its proposed forecasting models, makes significant contributions to energy policies to develop appropriate policies only for planning the amount of electricity usage needed in the future. In this context, this study emphasizes that renewable energy-based electricity generation transformation should be considered as an important strategic goal in terms of both environmental sustainability and energy security.

Violence against women is a significant sociological problem that negatively affects society. Although violence against women is widespread worldwide, the help-seeking behavior of women exposed to violence remains underdeveloped. In this study conducted in Turkey, the formal help-seeking behavior of women exposed to violence was studied according to sociodemographic factors. Data were obtained from surveys on domestic violence against women from the Turkish Statistical Institute (TURKSTAT), which was held in 2008 and 2014. Chi-square and binary logistic regression analyses are used in this study. The dependent variable is determined as whether women who had experienced violence sought help from formal institutions. In order to explain the dependent variable, education, age group, region, and sociodemographic variables are used. According to the results, as the education level of women exposed to violence increases, help-seeking behavior through official means also increases. In addition, women with a personal income are more likely to seek formal help than those without, and the development of the sociocultural region inhabited affects the formal help-seeking behavior of women exposed to violence. Between 2008 and 2014, legal regulations on women’s rights in Turkey were seen to positively affect formal help-seeking behavior. Although the formal help-seeking behavior of women subjected to violence in 2014 increased significantly compared to 2008, this improvement is not sufficient.

Enerji piyasalarının karmaşık yapısı ve bu piyasalarda oluşan fiyatların öngörülebilirliği hem ekonomik istikrar hem de sürdürülebilir kalkınma hedefleri açısından büyük önem taşımaktadır. Özellikle Türkiye gibi enerji talebinin hızla arttığı ülkelerde, piyasa takas fiyatının (PTF) doğru bir şekilde tahmin edilmesi, piyasa katılımcılarının etkin kararlar almasına olanak tanımakta ve enerji piyasasının şeffaflığını artırmaktadır. Bu çalışma, Türkiye enerji piyasasında PTF’nin tahmini için altı farklı makine öğrenimi algoritmasının (Doğrusal Regresyon, Karar Ağacı, Destek Vektör Makineleri, Gaussian Süreç Regresyonu, Çekirdek Tabanlı Regresyon ve Sinir Ağları) etkinliğini karşılaştırmalı olarak analiz etmektedir. Türkiye'nin Ocak 2012-Ocak 2023 dönemine ait enerji piyasası verilerinden yararlanılarak, sıcaklık, brüt elektrik tüketimi, toplam kurulu güç, döviz kuru ve Brent petrol fiyatları gibi faktörlerin PTF üzerindeki etkileri incelenmiştir. Sonuçlar, Gaussian Süreç Regresyonu (GPR) ve Karar Ağacı (DT) modellerinin PTF tahmininde diğer yöntemlere göre daha yüksek doğruluk sağladığını göstermektedir. GPR modeli, eğitim ve test aşamalarında tutarlı bir performans sergileyerek en düşük RMSE ve en yüksek R² değerlerine ulaşmıştır. Ayrıca, DT modeli test aşamasında istikrarlı sonuçlar sunarak ikinci en iyi performansı göstermiştir.