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Aiming at the problem that the Osprey Optimization Algorithm (OOA) does not have high optimization accuracy and is prone to falling into local optimum, an Improved Osprey Optimization Algorithm Based on a Two-Color Complementary Mechanism for Global Optimization (IOOA) is proposed. The core of the IOOA algorithm lies in its unique two-color complementary mechanism, which significantly improves the algorithm's global search capability and optimization performance. Firstly, in the initialization stage, the population is created by combining logistic chaos mapping and the good point set method, and the population is divided into four different color groups by drawing on the four-color theory to enhance the population diversity. Secondly, a two-color complementary mechanism is introduced, where the blue population maintains the OOA core exploration strategy to ensure the stability and efficiency of the algorithm; the red population incorporates the Harris Hawk heuristic strategy in the development phase to strengthen the ability of local minima avoidance; the green group adopts the strolling and wandering strategy in the searching phase to add stochasticity and maintain the diversity; and the orange population implements the optimized spiral search and firefly perturbation strategies to deepen the exploration and effectively perturb the local optimums, respectively, to improve the overall population diversity, effectively perturbing the local optimum to improve the performance of the algorithm and the exploration ability of the solution space as a whole. Finally, to validate the performance of IOOA, classical benchmark functions and CEC2020 and CEC2022 test sets are selected for simulation, and ANOVA is used, as well as Wilcoxon and Friedman tests. The results show that IOOA significantly improves convergence accuracy and speed and demonstrates high practical value and advantages in engineering optimization applications. [ABSTRACT FROM AUTHOR]

Aiming at the problems of sine cosine algorithm, such as easy to fall into local optimum, low accuracy and slow convergence speed, an improved sine cosine algorithm based on dynamic classification strategy is proposed. Firstly, the Latin hypercube sampling method is introduced to divide the search space evenly so that the initial population covers the whole search space to maintain the diversity of the initial population. Secondly, the dynamic classification strategy is used, according to the order of fitness value, the population dynamics is divided into three grades: good, medium and poor. The destruction strategy and elite guidance method are used to disturb the population dynamics, so as to improve the convergence accuracy of the algorithm and enhance the ability to jump out of the local optimum. Finally, the global search strategy of dynamic reverse learning is designed by introducing the reverse learning method to improve the convergence speed of the algorithm. At the same time, the performance of the improved algorithm in the aspects of complexity, convergence and stability is tested, and 15 standard test functions are selected to carry out the simulation experiment analysis in the low and high dimensional state, and compared with particle swarm optimization algorithm, backtracking search algorithm and other improved sine cosine algorithms. The simulation results show that the proposed algorithm can improve the convergence and stability of the algorithm. [ABSTRACT FROM AUTHOR]

Background/Aims: We aimed to evaluate the distribution of abnormal liver-related biomarkers in patients with coronavirus disease (COVID-19) and explore the prognostic value of elevated liver enzymes and abnormal liver synthetic capacity with regards to patient mortality. Patients and Methods: This retrospective observational study included 80 laboratory-confirmed COVID-19 cases. Data were collected from the electronic medical record system by a trained team of physicians. Alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin (TB), albumin, and prealbumin levels at admission and on day 7 after admission were collected. The primary outcome of the current study was patient mortality. Results: Abnormal ALT, AST, TB, albumin, and prealbumin levels were observed in 11 (13.8%), 15 (18.8%), 5 (6.3%), 22 (27.5%), and 31 (38.8%) patients, respectively. Male gender correlated with elevated ALT and AST levels (p = 0.027 and 0.036, respectively). Higher levels of AST and lower levels of albumin and prealbumin were associated with patient mortality (p = 0.009, 0.002, and 0.003, respectively). Multivariate Cox regression analysis identified patient age (p = 0.013, HR 1.108) and prealbumin levels (p = 0.015, HR 0.986) as independent predictors for patient mortality. However, changes in liver-related biomarkers were not associated with poor outcome in multivariate analysis (p > 0.05). Conclusions: Abnormalities in albumin and prealbumin levels are common among COVID-19 patients and hypoprealbuminemia independently predicts adverse outcome and should be carefully considered in clinical practice. Moreover, changes in liver-related biomarkers is not a salient feature of COVID-19. [ABSTRACT FROM AUTHOR]