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Abstract Although ablations are performed with conscious sedation or general anesthesia, microwave ablations can be painful post procedure. Newer analgesic modalities, including regional blocks, have promoted the proliferation of less invasive anesthesia care for ablative procedures. This study evaluates whether bilateral paravertebral blocks reduce the need for additional analgesics in comparison to unilateral blocks in microwave ablations. In this retrospective study, individuals undergoing microwave ablation who underwent unilateral versus bilateral nerve blocks at a single institution from 2017 to 2019 were compared. Categorical variables were analyzed using Pearson’s chi-squared tests. Comparisons of means were completed using multiple T-tests corrected using the Holm-Sidak method with $$\alpha $$ α = 0.05. Regression modeling was used to identify factors related to increased MME (milligram morphine equivalent) usage and post-procedure admission rates. A total of 106 patients undergoing 112 liver MWA procedures were included in this analysis, with patients receiving either a bilateral or unilateral block. Pre-procedural characteristics demonstrated no significant differences in age or gender. Bilateral blocks were associated with decreased usage of gabapentin (14% vs. 0%, p = 0.01) and a lower rate of post-procedure admissions (OR 0.23, p = 0.003). Therefore, when using paravertebral blocks, bilateral blocks are superior to unilateral blocks, as demonstrated by decreased rates of hospital admission and reduced use of systemic neuropathic pain medication. Additionally, reducing post-procedural MME may reduce the rate of admission to the hospital.

Giant cell tumor of the soft tissue (GCTST) is a neoplasm with low malignant potential and typically affects the trunk and extremities. Herein, we present a case of a palpable right neck mass diagnosed as a GCTST of the carotid sheath in a 38-year-old woman. A review of the imaging characteristics as well as of the differential diagnoses of primary neoplasms of the carotid space is presented.

A three-neuron network model with mixed delays involving multiple discrete and distributed delays is considered in this paper. Taking the discrete and distribute delays as bifurcation parameters respectively, we investigate the stability of the system structure and the conditions for the generation of Hopf bifurcation from the perspective of the distribution of the root of the characteristic equation of the linearized system at the equilibrium state of the nonlinear system. The intervals of parameters that make the system stable and unstable are also given. In addition, when the conditions of Hopf bifurcation theorem are satisfied, the calculation formulas for determining the direction of Hopf bifurcation and the stability of bifurcating periodic solution are presented by means of the central manifold theorem and normal form method. Finally, numerical experiments are carried out to support the correctness of the theoretical results in different cases. It can be concluded that the oscillation and instability caused by delays obviously affect the stability of the network.

In this paper, an economic emission dispatch (EED) model is developed to reduce fuel cost and environmental pollution emissions. Considering the development of new energy sources in recent years, the EED problem involves thermal units with the valve point effect and WTs. Meanwhile, it complies with demand constraint and generator capacity constraints. A recurrent neural network (RNN) is proposed to search for local optimal solution of the introduced nonconvex EED problem. The optimality and convergence of the proposed dynamic model are given. The RNN algorithm is verified on a power generation system for the optimization of scheduling and minimization of total cost. Moreover, a particle swarm optimization (PSO) algorithm is compared with RNN under the same problematic frame. Numerical simulation results demonstrate that the optimal scheduling given by RNN is more precise and has lower total cost than PSO. In addition, the dynamic variation of power load demand is considered and the power distribution of eight generators during 12 time periods is depicted.

Purpose: Proximal splenic artery embolization plays an important role in the treatment of hemodynamically stable blunt splenic trauma patients with medium- to high-grade injuries. Proximal splenic artery embolization is most often performed utilizing endovascular coils or vascular plugs. The objective of this study was to compare technical and clinical outcomes of proximal splenic artery embolization using either endovascular coils or vascular plugs in patients with traumatic splenic injuries. Materials and methods: A single-institution retrospective review of all proximal splenic artery embolizations for trauma over a 5-year period was performed. Patients who underwent embolization using both endovascular coils and vascular plugs were excluded. Baseline characteristics, including patient age, sex, and grade of splenic injury, were recorded. Complication rates, rates of splenic salvage, and total fluoroscopy time were recorded and compared. Results: A total of 26 patients were included in the analysis (17 males, 9 females, median age: 50 years). Of these, 15 patients were treated with vascular plugs (57.7%), while 11 patients (42.3%) were treated with endovascular coils. Mean grade of injury was 3.5 and 4.1 in the vascular plug and endovascular coils groups, respectively. There were no differences between the groups regarding these baseline characteristics. Splenic salvage was 100% in both groups. No major complications were identified in either group. Mean fluoroscopy time was significantly lower in the vascular plug group (14.5 versus 34.0 min; p

Discrete Fourier transform (DFT), inverse DFT (IDFT), and circular convolution are important tools for analyzing and designing discrete signals and systems, and are widely used in various industries. In order to pursue faster operational efficiencies or more accurate operational results, engineering calculations are often required to be quick and easy. Therefore, it is necessary to reduce the local computational overhead required to perform DFT, IDFT, and circular convolution. In addition to improving the algorithms themselves, cloud computing outsourcing is also an optional method. In this paper, in response to the new challenges brought by cloud computing outsourcing, we design and propose an efficient and secure cloud computing outsourcing protocol for DFT, IDFT, and circular convolution. Through a theoretical explanation and simulation experiment, we show the efficiency, security, and verifiability of the proposed protocol.

Pointcloud is a collection of 3D object coordinate systems in 3D scene. Generally, point data in pointclouds represent the outer surface of an object. It is widely used in 3D reconstruction applications in various fields. When obtaining pointcloud data from RGB-D images, if part of the information in the RGB-D images is lost or damaged, the pointcloud data will be hollow or too sparse. Moreover, it is not conducive to the subsequent application of pointcloud data. Based on the boundary of the region to be repaired, we proposes to repair the damaged image and synthesize the complete pointcloud data after a series of preprocessing steps related to the image. Experiments show that the our method can effectively improve the restoration of the lost details of the pixel in the target area and that it will have the fuller pointcloud data after synthesizing the restored image.

The consensus problem is investigated in this paper for multi-agent systems subjected to sampled data and packet losses. The switching system, which simultaneously contains both stable and unstable subsystems, is applied to model the packet dropout in a deterministic way. The focus of this paper is to derive an improved condition stated elegantly in terms of linear matrix inequality (LMI) with less conservative and lower complexity such that the multi-agent system considered can reach consensus. In line with this, a novel nonlinear function is introduced in the construction of the piecewise differentiable Lyapunov functions, and the Wirtinger inequality is applied as well. Furthermore, several comparison results against other related ones are presented, which yield that our consensus protocols significantly improve upon the existing ones advocated thus far. We verify the validity, the effectiveness, and the practical applicability of our results in the simulation examples.

This paper considers the fixed-time synchronization (FIXTS) of neural networks (NNs) by using quantized intermittent control (QIC). Based on QIC, a fixed-time controller is designed to ensure that the NNs achieve synchronization in finite time. With this controller, the settling time can be estimated regardless of initial conditions. After ensuring that the system has stabilized through this strategy, it is suitable for image protection given the behavior of the system. Meanwhile, the encryption effect of the image depends on the encryption algorithm, and the quality of the decrypted image depends on the synchronization error of NNs. The numerical results show that the designed controller is effective and validate the practical application of FIXTS of NNs in image protection.

In order to avoid the risk of the biological database being attacked and tampered by hackers, an Autoassociative Memory (AAM) model is proposed in this paper. The model is based on the recurrent neural networks (RNNs) for face recognition, under the condition that the face database is replaced by its model parameters. The stability of the model is proved and analyzed to slack the constraints of AAM model parameters. Besides, a design procedure about solving AAM model parameters is given, and the face recognition method by AAM model is established, which includes image preprocessing, AAM model training, and image recognition. Finally, simulation results on two experiments show the feasibility and performance of the proposed face recognition method.

Wolfram divided the 256 elementary cellular automata rules informally into four classes using dynamical concepts like periodicity, stability, and chaos. Rule 24, which is Bernoulli στ-shift rule and is member of Wolfram’s class II, is said to be simple as periodic before. Therefore, it is worthwhile studying dynamical behaviors of four rules, whether they possess chaotic attractors or not. In this paper, the complex dynamical behaviors of rule 24 of one-dimensional cellular automata are investigated from the viewpoint of symbolic dynamics. We find that rule 24 is chaotic in the sense of both Li-Yorke and Devaney on its attractor. Furthermore, we prove that four rules of global equivalence ε52 of cellular automata are topologically conjugate. Then, we use diagrams to explain the attractor of rule 24, where characteristic function is used to describe the fact that all points fall into Bernoulli-shift map after two iterations under rule 24.

The problem of projective lag synchronization of coupled neural networks with time delay is investigated. By means of the Lyapunov stability theory, an intermittent controller is designed for achieving projective lag synchronization between two delayed neural networks systems. Numerical simulations on coupled Lu neural systems illustrate the effectiveness of the results.

Different from the most existing results, in this paper an intermittent control scheme is designed to achieve lag synchronization of coupled hyperchaotic systems. Several sufficient conditions ensuring lag synchronization are proposed by rigorous theoretical analysis with the help of the Lyapunov stability theory. Numerical simulations are also presented to show the effectiveness and feasibility of the theoretical results.