Does Preoperative Cognitive Optimization Improve Postoperative Outcomes in the Elderly?

Perioperative neurocognitive disorder (PND) is a growing concern, affecting several million elderly patients each year in the United States, but strategies for its effective prevention have not yet been established. Humeidan et al. recently demonstrated that preoperative brain exercise resulted in a decrease in postoperative delirium incidence in elderly surgical patients, suggesting the potential of presurgical cognitive optimization to improve postoperative cognitive outcomes. This brief review summarizes the current knowledge regarding preoperative cognitive optimization and highlights landmark studies, as well as current ongoing studies, as the field is rapidly growing. This review further discusses the benefit of cognitive training in non-surgical elderly populations and the role of cognitive training in patients with preexisting cognitive impairment or dementia. The review also examines preclinical evidence in support of cognitive training, which can facilitate understanding of brain plasticity and the pathophysiology of PND. The literature suggests positive impacts of presurgical cognitive optimization, but further studies are encouraged to establish effective cognitive training programs for elderly presurgical patients.

A Combined Model Based on Society Cognitive Optimization Algorithm for Wind Speed Forecasting

Wind energy, as renewable energy, has drawn the attention of society. The use of wind power generation can reduce the pollution to the environment and solve the problem of power shortage in offshore islands, grassland, pastoral areas, mountain areas, and highlands. Wind speed forecasting plays a significant role in wind farms. It can improve economic and social benefits and make an operation schedule for wind turbines in large wind farms. At present, researchers have proposed a variety of methods for wind speed forecasting; artificial neural network (ANN) is one of the most commonly used methods. This paper proposes a combined model based on the existing artificial neural network algorithms for wind speed forecasting at different heights. We first use the wavelet threshold method to the original wind speed data set for noise reduction. After that, the three artificial neural networks, extreme learning machine (ELM), Elman neural network, and Long Short-Term Memory neural network (LSTM), are applied for wind speed forecasting. In addition, variance reciprocal method and society cognitive optimization algorithm (SCO) are used to optimize the weight coefficients of the combined model. In order to evaluate the forecasting performance of the combined model, we select wind speed data at three heights (20m, 50m, and 80m) in National Wind Technology Center M2 Tower. The experimental results show that the forecasting performance of the combined model is better than the single model, and it has a good forecasting performance for the wind speed at different heights.

Formation Generation for Multiple Unmanned Vehicles Using Multi-Agent Hybrid Social Cognitive Optimization Based on the Internet of Things

Multi-agent hybrid social cognitive optimization (MAHSCO) based on the Internet of Things (IoT) is suggested to solve the problem of the generation of formations of unmanned vehicles. Through the analysis of the unmanned vehicle formation problem, formation principles, formation scale, unmanned vehicle formation safety distance, and formation evaluation indicators are taken into consideration. The application of the IoT enables the optimization of distributed computing. To ensure the reliability of the formation algorithm, the convergence of MAHSCO has been proved. Finally, computer simulation and actual unmanned aerial vehicle (UAV) formation generation flight generating four typical formations are carried out. The result of the actual UAV formation generation flight is consistent with the simulation experiment, and the algorithm performs well. The MAHSCO algorithm based on the IoT is proved to be able to generate formations that meet the mission requirements quickly and accurately.