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The Make-up Air Unit (MAU) is an air conditioning system which plays an important role in serving semiconductor cleanrooms. It provides constant temperature and humidity for fresh air through various sections, including fresh air filtration, preheating, precooling, humidification, recooling, reheating, air supply, and high-efficiency filtration. However, the commonly used PID control method of the MAU indicates a deficiency in energy consumption. Hence, this research introduces a proactive energy-saving optimization control method based on machine learning and intelligent optimization algorithms. Firstly, the machine learning methods are used to train historical data of the MAU, resulting in a data-driven prediction model of energy consumption for the system. Subsequently, the customized genetic algorithm (GA) is used to optimize energy in cold and hot water systems. It facilitates the dynamic adjustment of the regulating valve opening for the cold and hot water coil in the fresh air unit, responding to real-time variations in outdoor air conditions. Meanwhile, it ensures that the supply air temperature and humidification adhere to specified requirements, thereby reducing the energy consumption associated with cold and hot water usage in the MAU. The experimental results indicate that the proposed algorithm can provide significant energy conservation in the MAU. [ABSTRACT FROM AUTHOR]

Due to the complex nature and highly heterogeneous of cancer, as well as different pathogenesis and clinical features among different cancer subtypes, it was crucial to identify cancer subtypes in cancer diagnosis, prognosis, and treatment. The rapid developments of high-throughput technologies have dramatically improved the efficiency of collecting data from various types of omics. Also, integrating multi-omics data related to cancer occurrence and progression can lead to a better understanding of cancer pathogenesis, subtype prediction, and personalized treatment options. Therefore, we proposed an efficient multi-omics bipartite graph subspace learning anchor-based clustering (MBSLC) method to identify cancer subtypes. In contrast, the bipartite graph intended to learn cluster-friendly representations. Experiments showed that the proposed MBSLC method can capture the latent spaces of multi-omics data effectively and showed superiority over other state-of-the-art methods for cancer subtype analysis. Moreover, the survival and clinical analyses further demonstrated the effectiveness of MBSLC. The code and datasets of this paper can be found in https://github.com/Julius666/MBSLC. [ABSTRACT FROM AUTHOR]

In Pareto-based many-objective evolutionary algorithms, performance usually degrades drastically as the number of objectives increases due to the poor discriminability of Pareto optimality. Although some relaxed Pareto domination relations have been proposed to relieve the loss of selection pressure, it is hard to maintain good population diversity, especially in the late phase of evolution. To solve this problem, we propose a symmetrical Generalized Pareto Dominance and Adjusted Reference Vectors Cooperative (GPDARVC) evolutionary algorithm to deal with many-objective optimization problems. The symmetric version of generalized Pareto dominance (GPD), as an efficient framework, provides sufficient selection pressure without degrading diversity, no matter of the number of objectives. Then, reference vectors (RVs), initially generated evenly in the objective space, guide the selection with good diversity. The cooperation of GPD and RVs in environmental selection in part ensures a good balance of convergence and diversity. Also, to further enhance the effectiveness of RV-guided selection, we regenerate more RVs according to the proportion of valid RVs; thereafter, we select the most valid RVs for adjustment after the association operation. To validate the performance of GPDARVC, we compare it with seven representative algorithms on commonly used sets of problems. This comprehensive analysis results in 26 test problems with different objective numbers and 6 practical problems, which show that GPDARVC outperforms other algorithms in most cases, indicating its great potential to solve many-objective optimization problems. [ABSTRACT FROM AUTHOR]