In lung nodule puncture and abdominal radiotherapy, the motion of lesions caused by patient respiration can compromise procedural accuracy. Consequently, integrating respiratory prediction can significantly enhance surgical precision. However, traditional methods often fail to provide real-time capabilities and may inadequately represent the dynamic characteristics of respiratory signals, resulting in potential inaccuracies and inefficiencies. In this study, we aimed to develop a novel real-time respiratory prediction model that incorporates respiratory signals along with their first- and second-order derivatives within a recurrent neural network framework. By incorporating differential signals, our model effectively learns the high-frequency characteristics of respiratory signals, enhancing its sensitivity to short-term respiratory fluctuations while preserving the ability to capture long-term dependencies. This approach addresses the limitations of current methods in handling nonlinear, quasi-periodic, and nonstationary respiratory signals. Our model achieved superior performance across prediction windows of 200, 400, and 600 ms, with mean absolute errors of 0.026, 0.216, and 0.394 and root mean squared errors of 0.034, 0.288, and 0.534, respectively. This study enhances the precision of respiratory prediction and delineates the critical role of differential signals in forecasting quasi-periodic physiological patterns.

Flash-based SSDs have been extensively deployed in modern storage systems to satisfy the increasing demand of storage performance and energy efficiency. However, Garbage Collection (GC) is an important performance concern for flash-based SSDs, because it tends to disrupt the normal operations of an SSD. This problem continues to plague flash-based storage systems, particularly in the high performance computing and enterprise environment. An important root cause for this problem, as revealed by previous studies, is the serious contention for the flash resources and the severe mutually adversary interference between the user I/O requests and GC-induced I/O requests. The on-board buffer cache within SSDs serves to play an essential role in smoothing the gap between the upper-level applications and the lower-level flash chips and alleviating this problem to some extend. Nevertheless, the existing cache replacement algorithms are well optimized to reduce the miss rate of the buffer cache by reducing the I/O traffic to the flash chips as much as possible, but without considering the GC operations within the flash chips. Consequently, they fail to address the root cause of the problem and thus are far from being sufficient and effective in reducing the expensive I/O traffic to the flash chips that are in the GC state. To address this important performance issue in flash-based storage systems, particularly in the HPC and enterprise environment, we propose a Garbage Collection aware Replacement policy, called GCaR, to improve the performance of flash-based SSDs. The basic idea is to give higher priority to caching the data blocks belonging to the flash chips that are in the GC state. This substantially lessens the contentions between the user I/O operations and the GC-induced I/O operations. To verify the effectiveness of GCaR, we have integrated it into the SSD extended Disksim simulator. The simulation results show that GCaR can significantly improve the storage performance by up to 40.7 percent in terms of the average response times.

Garbage Collection (GC) is an important performance concern for flash-based SSDs, because it tends to disrupt the normal operations of an SSD. This problem continues to plague flash-based storage systems, particularly in the high performance computing and enterprise environment. An important root cause for this problem, as revealed by previous studies, is the serious contention for the flash resources and the severe mutually adversary interference between the user I/O requests and GC-induced I/O requests. The on-board buffer cache within SSDs serves to play an essential role in smoothing the gap between the upper-level applications and the lower-level flash chips and alleviating this problem to some extend. Nevertheless, the existing cache replacement algorithms are well optimized to reduce the miss rate of the buffer cache by reducing the I/O traffic to the flash chips as much as possible, but without considering the GC operations within the flash chips. Consequently, they fail to address the root cause of the problem and thus are far from being sufficient and effective in reducing the expensive I/O traffic to the flash chips that are in the GC state.

Web community detection is one of the important ways to enhance retrieval quality of web search engine. How to design one highly effective algorithm to partition network community with few domain knowledge is the key to network community detection. Traditional algorithms, such as Wu-Huberman algorithm, need priori information to detect community, the Radichi algorithm relies on the triangle number in the network, the extremal optimization algorithm proposed by Duch J. is extremely sensitive to the initial solution, easy to fall into the local optimum. This article proposes a new model based on particle swarm optimization to detect network community, and with different scale network chart, Zachary, Krebs and dolphins network architecture to test the algorithm, the experimental results indicate this model can effectively find web communities of network structure without any domain information.