Naval Academy

Financial performance analysis is of great importance to companies, as it reveals a company's financial health, profitability, and sustainability. In this study, the financial performance analysis of ten different energy companies listed on the Borsa İstanbul Index was analyzed using MCDM (Multi-Criteria Decision Making) methods, and trend analysis was performed using ML (Machine Learning) techniques. The weighting of the criteria, obtained from previous studies was determined using the MEREC (Method based on the Removal Effects of Criteria) method, and the alternative ranking was performed using the Fuzzy Einstein TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method. The present study is distinguished from other studies in the literature by virtue of its utilization of MCDM techniques and ML.

Comorbid insomnia and sleep apnea (COMISA) is a frequent but underrec-ognized condition in patients with obstructive sleep apnea (OSA). While OSA is strongly linked to hypertension, the independent contribution of COMISA to resistant hyper-tension (RH) remains unclear. This study aimed to investigate the association between COMISA and RH in hypertensive OSA patients and to identify independent predictors of RH. This retrospective cross-sectional study included 131 patients diagnosed with both OSA and hypertension who underwent full-night polysomnography (PSG) at a ter- tiary sleep center. The Insomnia Severity Index (ISI) was used to define COMISA (ISI ≥15). Resistant hypertension (RH) was defined as uncontrolled blood pressure despite the use of at least 3 antihypertensive agents of different classes, including a diuretic. Demographic, clinical, and polysomnographic data were analyzed using multiple logistic regression to determine independent predictors of RH. Of 131 hypertensive OSA patients, 39 (29.8%) met criteria for COMISA. The preva-lence of RH was 43.5%. COMISA was significantly more frequent in the RH group (66.7% vs. 33.3%, P = .006). In the multiple logistic regression analysis, COMISA (OR = 5.26, P < .001, 95% CI: 2.04-13.57) and male sex (OR = 3.24, P = .010, 95% CI: 1.36-7.72) were identi-fied as independent predictors of RH, while age, apnea-hypopnea index (AHI), and body mass index (BMI) were not significantly associated. Comorbid insomnia and sleep apnea (COMISA) markedly increases the risk of RH in hypertensive OSA patients, independent of apnea severity and obesity. These find-ings highlight COMISA as a distinct cardiovascular phenotype within the OSA spectrum. Routine screening and targeted treatment of insomnia in OSA may represent a critical approach to improving blood pressure control and cardiovascular outcomes.

Portfolio optimisation with cardinality constraints has been extensively studied in the realm of financial investment, recognised as an NP-hard quadratic programming problem. As an innovative metaheuristic approach, the dung beetle optimiser leverages its unique optimisation search mechanism to effectively tackle unconstrained optimisation problems. However, the realities of portfolio optimisation involve various constraints; thus, the original dung beetle optimiser may not suffice. Consequently, this study develops an improved dung beetle optimiser to address cardinality constrained portfolio optimisation, incorporating a new decision variable update strategy, a constraint handling strategy, and a local search strategy. These techniques facilitate the efficient selection of assets from among multiple candidate assets. To validate the capabilities of the indicated methodologies, five datasets from OR-Library and six datasets from NGINX are employed for testing. The results from these datasets consistently indicate that the proposed strategies outperform existing alternatives. Furthermore, the comparison results with various methods presented in other works demonstrate that the proposed technology is competitive in the realm of cardinality constrained portfolio optimisation.

This study aimed to determine the culture conditions and some anatomic features for using white worms (Enchytraeus sp.) as live feed in aquaculture. In the first experiment (Culture 1), coconut peat was used as the culture media and it was determined that, under approximately 21 °C, the population of the white worm species increased from 10 adult individuals to a total of 424 individuals (including both adults and juveniles) over a 70-day period. Unlike the first experimental stage (Culture 1), the second stage (Culture 2) was structured into three groups. In the second experiment (Culture 2), coconut peat was used as the culture media in the first (G1) and second experimental groups (G2), and zeolite with coconut peat in the third group (G3). The first (G1) and third experimental (G3) groups were fed a control diet, while the second group (G2) was fed a diet with 10% zeolite added to the control diet. In the 60-day study conducted during the culture-2 phase, the worm population, which initially consisted of 10 individuals, reached its highest values in Group 3. The use of zeolite as a substrate and feed additive for white worm culture was investigated for the first time, and original data were obtained that will contribute to filling the gap in the literature. Furthermore, the aquarium filtration sponge used in addition to the culture soil was determined to be a suitable substrate material for collecting cocoon data. The findings of this study provide new supporting data that will fill gaps in the literature regarding materials and methods that contribute positively to white worm culture conditions.