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A recent report from Universiti Malaya discusses research on cancer treatment using optimal control supported by swarm intelligence. The study aims to develop hybrid techniques that combine optimal control theory with evolutionary algorithms and multi-objective swarm algorithms to reduce cancerous cells while minimizing the use of chemotherapy medications and toxicity. Two hybrid techniques were proposed and evaluated, with MOEA/D emerging as the most effective algorithm in reducing tumor size and cancer drug concentration. The study highlights the importance of integrating optimal control theory and evolutionary algorithms for optimizing cancer chemotherapy treatment. [Extracted from the article]

Researchers from Babes-Bolyai University in Cluj-Napoca, Romania have developed a novel null model called MuAn that uses Evolutionary Algorithms to generate synthetic networks with high values of Assortativity Degree and Local Clustering Coefficient, which are important for identifying network motifs. The proposed model has been shown to be effective in generating networks with motifs and can be used in various domains such as gene regulatory networks, drug-target networks, and artificial neuronal networks. This research has been peer-reviewed and published in Swarm and Evolutionary Computation. [Extracted from the article]

Researchers from Gannan Normal University in Jiangxi, China have published a new report on the use of supercritical carbon dioxide in biotechnology. The researchers developed a multi-scale computational model called CADPPD-EA, which simulates the dissolution behavior of supercritical carbon dioxide in polymer fluids. The model showed good computational effects and outperformed similar models in terms of accuracy, efficiency, and correlation. The researchers believe that the CADPPD-EA model can be applied in various fields such as chemical engineering, material energy, biomedicine, and computer science. [Extracted from the article]

A recent study conducted at Anhui University in China has proposed a new algorithm called the adaptive conjugate gradient accelerated evolutionary algorithm (ACG-EA) for optimizing cancer treatment plans in intensity-modulated proton therapy. This technique is considered one of the most advanced cancer treatment methods due to the unique characteristics of proton radiation. The ACG-EA algorithm aims to generate diverse high-quality treatment plans simultaneously, addressing the limitations of existing optimization methods. Experimental results have shown promising performance in six clinical cancer cases. This research has been peer-reviewed and published in the journal Applied Soft Computing. [Extracted from the article]

Researchers from the Universidad Tecnologica de Leon have developed a novel strategy for automatically classifying coronary stenosis using an evolutionary algorithm. The method involves a feature extraction stage to form a bank of 473 features, and the feature selection task is carried out on this high-dimensional feature bank. The researchers achieved a 99% discrimination rate with a subset of four features, and the classification performance was found to be suitable for use in a clinical decision support system. This research provides valuable insights into the automatic classification of coronary stenosis and could have implications for improving diagnosis and treatment. [Extracted from the article]