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Microgrids (MGs) have evolved as critical components of modern energy distribution networks, providing increased dependability, efficiency, and sustainability. Effective control strategies are essential for optimizing MG operation and maintaining stability in the face of changing environmental and load conditions. Traditional rule-based control systems are extensively used due to their interpretability and simplicity. However, these strategies frequently lack the flexibility for complex and changing system dynamics. This paper provides a novel method called hybrid intelligent control for adaptive MG that integrates basic rule-based control and deep learning techniques, including gated recurrent units (GRUs), basic recurrent neural networks (RNNs), and long short-term memory (LSTM). The main target of this hybrid approach is to improve MG management performance by combining the strengths of basic rule-based systems and deep learning techniques. These deep learning techniques readily enhance and adapt control decisions based on historical data and domain-specific rules, leading to increasing system efficiency, stability, and resilience in adaptive MG. Our results show that the proposed method optimizes MG operation, especially under demanding conditions such as variable renewable energy supply and unanticipated load fluctuations. This study investigates special RNN architectures and hyperparameter optimization techniques with the aim of predicting power consumption and generation within the adaptive MG system. Our promising results show the highest-performing models indicating high accuracy and efficiency in power prediction. The finest-performing model accomplishes an R 2 value close to 1, representing a strong correlation between predicted and actual power values. Specifically, the best model achieved an R 2 value of 0.999809, an MSE of 0.000002, and an MAE of 0.000831. [ABSTRACT FROM AUTHOR]

The aim of this study is to analyze the performance of BIST cement sector companies in terms of selected indicators with integrated CRITIC and MABAC methods. In the study, the data of cement companies from 2014 to 2018 are analyzed by multi criteria decision-making methods. According to the results of the CRITIC method, it has been determined that the most important performance criterion for these companies varies by years. In addition to this, according to the MABAC method, although ADANA, ADBGR and KONYA are the three most financially successful companies, ADNAC, ADANA and ADBGR are the three most successful companies in terms of stock returns. In addition, whether there is any significant relationship between MABAC performance ranking and stock return ranking is examined by Spearman correlation analysis. According to the correlation analysis findings, there is a positive and moderately significant relationship between the suggested MABAC performance ranking and the yield ranking [ABSTRACT FROM AUTHOR]

Prostate cancer is the main problem in advanced aged population and it is the major reason causing death in western countries. Frequently the tumor metastasis or locally invades tissues when it is diagnosed. The main target should be the early diagnosis of the tumor. Today, the most important precursor lesion of prostate cancer is prostate intraepithelial neoplasm (PIN) in pathological analysis. This study aims to discuss PIN diagnostic value. This study comprimised on retrospectical determination of prostate specific antigen (PSA) levels and/or digital rectal examination (DRE) and sextant biopsies with the guide of transrectal ultrasonography (TRUS). Between April 2001 and September 2003, 166 patients were included in the study. Follow-up period was 31 months. Pathological results of prostate cancer, high grade PIN (hgPIN), low grade PIN (lgPIN) BPH and age, average serum PSA levels, DRE findings, Gleason Score and Gleason Grade results for prostate cancer patients were compared. In conclusion, the patients who had hgPIN lesions with BPH adviced for periodic follow up for prostate specific antigen (PSA) and digital rectal examination (DRE) and in necessary conditions, rebiopsy. [ABSTRACT FROM AUTHOR]