Application of multimodal standardized analgesia under the concept of enhanced recovery after surgery in laparoscopic radical colorectal cancer surgery

AimsTo observe the efficacy and safety of multimodal standardized analgesia in patients undergoing laparoscopic radical colorectal cancer surgery.MethodsA prospective, double-blind, randomized study of patients who were admitted to our hospital between December 2020 and March 2022 with a diagnosis of colorectal cancer and who intended to undergo elective laparoscopic radical colorectal cancer surgery was conducted. The participants were randomly divided into two intervention groups, namely, a multimodal standardized analgesia group and a routine analgesia group. In both groups, the visual analogue scale (VAS) pain scores while resting at 6 h, 24 h, 48 h and 72 h and during movement at 24 h, 48 h and 72 h; the number of patient controlled intravenous analgesia (PCIA) pump button presses and postoperative recovery indicators within 3 days after surgery; the interleukin-6 (IL-6) and C-reactive protein (CRP) levels on the 1st and 4th days after surgery; and the incidence of postoperative adverse reactions and complications were recorded.ResultsCompared with the control group, the multimodal standardized analgesia group had significantly lower VAS pain scores at different time points while resting and during movement (P<0.05), significantly fewer PCIA pump button presses during the first 3 postoperative days (P<0.05), and significantly lower IL-6 and CRP levels on the 1st postoperative day (P<0.05). There was no statistically significant difference in the time to out-of-bed activity, the time to first flatus, the IL-6 and CRP levels on the 4th postoperative day or the incidence of postoperative adverse reactions and complications between the two groups (P >0.05).ConclusionFor patients undergoing laparoscopic radical colorectal cancer surgery, multimodal standardized analgesia with ropivacaine combined with parecoxib sodium and a PCIA pump had a better analgesic effect, as it effectively inhibited early postoperative inflammatory reactions and promoted postoperative recovery and did not increase the incidence of adverse reactions and complications. Therefore, it is worthy of widespread clinical practice

An Improved Quantum-Behaved Particle Swarm Optimization Algorithm Combined with Reinforcement Learning for AUV Path Planning

In order to solve the problem of fast path planning and effective obstacle avoidance for autonomous underwater vehicles (AUVs) in two-dimensional underwater environment, a path planning algorithm based on deep Q-network and Quantum particle swarm optimization (DQN-QPSO) was proposed. Five actions are defined first: normal, exploration, particle explode, random mutation, and fine-tuning operation. After that, the five actions are selected by DQN decision thinking, and the position information of particles is dynamically updated in each iteration according to the selected actions. Finally, considering the complexity of underwater environment, the fitness function is designed, and the route length, deflection angle, and the influence of ocean current are considered comprehensively, so that the algorithm can find the solution path with the shortest energy consumption in underwater environment. Experimental results show that DQN-QPSO algorithm is an effective algorithm, and its performance is better than traditional methods

Real-Valued Weighted Subspace Fitting Algorithm for DOA Estimation with Block Sparse Recovery

In this paper, the problem of direction-of-arrival (DOA) estimation for strictly noncircular sources under the condition of unknown mutual coupling is concerned, and then a robust real-valued weighted subspace fitting (WSF) algorithm is proposed via block sparse recovery. Inspired by noncircularity, the real-valued coupled extended array output with double array aperture is first structured via exploiting the real-valued conversion. Then, an efficient real-valued block extended sparse recovery model is constructed by performing the parameterized decoupling operation to avoid the unknown mutual coupling and noncircular phase effects. Thereafter, the WSF framework is investigated to recover the real-valued block sparse matrix, where the spectrum of real-valued NC MUSIC-like is utilized to design a weighted matrix for strengthening the solutions sparsity. Eventually, DOA estimation is achieved based on the support set of the reconstructed block sparse matrix. Owing to the combination of noncircularity, parametrized decoupling thought, and reweighted strategy, the proposed method not only effectively achieves high-precision estimation, but also efficiently reduces the computational complexity. Plenty of simulation results demonstrate the effectiveness and efficiency of the proposed method

Joint Angle and Range Estimation in Monostatic FDA-MIMO Radar via Compressed Unitary PARAFAC

In this paper, we study the joint range and angle estimation problem based in monostatic frequency diverse-array multiple-input multiple-output (FDA-MIMO) radar, and propose a method for range and angle estimation base on compressed unitary parallel factor (PARAFAC). First, the received complex signal matrix is stacked into a third-order complex signal tensor. Then, we can transform the obtained third-order complex signal tensor into a third-order real-valued signal tensor by employing forward–backward and unitary transformation techniques. Next, a smaller third-order real-valued signal tensor is composed by using compressing the third-order real-valued signal tensor. After that, PARAFAC decomposition is applied to obtain the direction matrix. Lastly, the angle and range are estimated by employing the least square (LS) fitting. The estimation error of the proposed method is about 10% lower than that of the traditional PARAFAC method under the low number of snapshots. When the number of snapshots is high, the performance of the two methods is close. Moreover, the computational complexity of the proposed method is nearly 96% less than those of the traditional PARAFAC methods in the case of low snapshots, while the gap is larger in the case of high snapshots. The superiority and effectiveness of the method are proved by complexity analysis and simulation experiments

Robust Sparse Bayesian Learning Scheme for DOA Estimation with Non-Circular Sources

In this paper, a robust DOA estimation scheme based on sparse Bayesian learning (SBL) for non-circular signals in impulse noise and mutual coupling (MC) is proposed. Firstly, the Toeplitz property of the MC matrix is used to eliminate the effect of array MC, and the array aperture is extended by using the properties of the non-circular signal. To eliminate the effect of impulse noise, the outlier part of the impulse noise is reconstructed together with the original signal in the signal matrix, and the DOA coarse estimation is obtained by balancing the accuracy and efficiency of parameter estimation using the alternating SBL update algorithm. Finally, a one-dimensional search is used in the vicinity of the searched spectral peaks to achieve a high-precision DOA estimation. The effectiveness and robustness of the algorithm for dealing with the above errors are demonstrated by extensive simulations

Robust Sparse Bayesian Learning Scheme for DOA Estimation with Non-Circular Sources

In this paper, a robust DOA estimation scheme based on sparse Bayesian learning (SBL) for non-circular signals in impulse noise and mutual coupling (MC) is proposed. Firstly, the Toeplitz property of the MC matrix is used to eliminate the effect of array MC, and the array aperture is extended by using the properties of the non-circular signal. To eliminate the effect of impulse noise, the outlier part of the impulse noise is reconstructed together with the original signal in the signal matrix, and the DOA coarse estimation is obtained by balancing the accuracy and efficiency of parameter estimation using the alternating SBL update algorithm. Finally, a one-dimensional search is used in the vicinity of the searched spectral peaks to achieve a high-precision DOA estimation. The effectiveness and robustness of the algorithm for dealing with the above errors are demonstrated by extensive simulations

Fast Target Localization in FMCW-MIMO Radar with Low SNR and Snapshot via Multi-DeepNet

Frequency modulated continuous wave (FMCW) multiple-input multiple-output (MIMO) radars are widely applied in target localization. However, during the process, the estimation accuracy decreases sharply without considerable signal-to-noise ratio (SNR) and sufficient snapshot number. It is therefore necessary to consider estimation schemes that are valid under low signal-to-noise ratio (SNR) and snapshot. In this paper, a fast target localization framework based on multiple deep neural networks named Multi-DeepNet is proposed. In the scheme, multiple interoperating deep networks are employed to achieve accurate target localization in harsh environments. Firstly, we designed a coarse estimate using deep learning to determine the interval where the angle is located. Then, multiple neural networks are designed to realize accurate estimation. After that, the range estimation is determined. Finally, angles and ranges are matched by comparing the Frobenius norm. Simulations and experiments are conducted to verify the efficiency and accuracy of the proposed framework