Adverse reactions to cosmetic implants after COVID-19 vaccination: A literature review.

BACKGROUND: As the world's population of people vaccinated with the COVID-19 vaccine increases, adverse reactions are increasingly being reported. There have been progressive reports of the effects of COVID-19 vaccination on cosmetic fillers or prostheses, but they have not been reviewed based on their clinical morphologic patterns. This article reviewed the progress of research on adverse reactions to cosmetic implants after COVID-19 vaccination. METHODS: We researched the English-language literature up to October 15, 2022, using predefined keywords to identify relevant studies about adverse reactions to cosmetic implants after the COVID-19 vaccination, collecting patient characteristics, implant type, the time interval between vaccination and implantation or injection, time of onset, symptoms, treatments, and outcomes. RESULTS: Among the adverse reactions to implants associated with COVID-19 vaccination, we distinguished between (1) injectable fillers and (2) surgical prosthetic implants. The most common adverse reactions were at the site of hyaluronic acid injection and breast prosthesis after Pfizer vaccination, mainly DIRs, and mainly manifested as edema, rash, fever, and capsular contracture. This paper also reported the possible causes, treatments of DIRs, and limitations of current studies. CONCLUSIONS: In this article, we attempted to investigate and discuss all the adverse reactions of cosmetic implants related to COVID-19 vaccination in the current literature, to unmask these reactions and make a more accurate assessment of vaccine safety.

Using ELM-based weighted probabilistic model in the classification of synchronous EEG BCI.

Extreme learning machine (ELM) is an effective machine learning technique with simple theory and fast implementation, which has gained increasing interest from various research fields recently. A new method that combines ELM with probabilistic model method is proposed in this paper to classify the electroencephalography (EEG) signals in synchronous brain-computer interface (BCI) system. In the proposed method, the softmax function is used to convert the ELM output to classification probability. The Chernoff error bound, deduced from the Bayesian probabilistic model in the training process, is adopted as the weight to take the discriminant process. Since the proposed method makes use of the knowledge from all preceding training datasets, its discriminating performance improves accumulatively. In the test experiments based on the datasets from BCI competitions, the proposed method is compared with other classification methods, including the linear discriminant analysis, support vector machine, ELM and weighted probabilistic model methods. For comparison, the mutual information, classification accuracy and information transfer rate are considered as the evaluation indicators for these classifiers. The results demonstrate that our method shows competitive performance against other methods.

Robust dead reckoning system for mobile robots based on particle filter and raw range scan.

Robust dead reckoning is a complicated problem for wheeled mobile robots (WMRs), where the robots are faulty, such as the sticking of sensors or the slippage of wheels, for the discrete fault models and the continuous states have to be estimated simultaneously to reach a reliable fault diagnosis and accurate dead reckoning. Particle filters are one of the most promising approaches to handle hybrid system estimation problems, and they have also been widely used in many WMRs applications, such as pose tracking, SLAM, video tracking, fault identification, etc. In this paper, the readings of a laser range finder, which may be also interfered with by noises, are used to reach accurate dead reckoning. The main contribution is that a systematic method to implement fault diagnosis and dead reckoning in a particle filter framework concurrently is proposed. Firstly, the perception model of a laser range finder is given, where the raw scan may be faulty. Secondly, the kinematics of the normal model and different fault models for WMRs are given. Thirdly, the particle filter for fault diagnosis and dead reckoning is discussed. At last, experiments and analyses are reported to show the accuracy and efficiency of the presented method.

A dynamic hybrid framework for constrained evolutionary optimization.

Based on our previous work, this paper presents a dynamic hybrid framework, called DyHF, for solving constrained optimization problems. This framework consists of two major steps: global search model and local search model. In the global and local search models, differential evolution serves as the search engine, and Pareto dominance used in multiobjective optimization is employed to compare the individuals in the population. Unlike other existing methods, the above two steps are executed dynamically according to the feasibility proportion of the current population in this paper, with the purpose of reasonably distributing the computational resource for the global and local search during the evolution. The performance of DyHF is tested on 22 benchmark test functions. The experimental results clearly show that the overall performance of DyHF is highly competitive with that of a number of state-of-the-art approaches from the literature.

Constrained evolutionary optimization by means of (µ + λ)-differential evolution and improved adaptive trade-off model.

This paper proposes a (µ + λ)-differential evolution and an improved adaptive trade-off model for solving constrained optimization problems. The proposed (µ + λ)-differential evolution adopts three mutation strategies (i.e., rand/1 strategy, current-to-best/1 strategy, and rand/2 strategy) and binomial crossover to generate the offspring population. Moreover, the current-to-best/1 strategy has been improved in this paper to further enhance the global exploration ability by exploiting the feasibility proportion of the last population. Additionally, the improved adaptive trade-off model includes three main situations: the infeasible situation, the semi-feasible situation, and the feasible situation. In each situation, a constraint-handling mechanism is designed based on the characteristics of the current population. By combining the (µ + λ)-differential evolution with the improved adaptive trade-off model, a generic method named (µ + λ)-constrained differential evolution ((µ + λ)-CDE) is developed. The (µ + λ)-CDE is utilized to solve 24 well-known benchmark test functions provided for the special session on constrained real-parameter optimization of the 2006 IEEE Congress on Evolutionary Computation (CEC2006). Experimental results suggest that the (µ + λ)-CDE is very promising for constrained optimization, since it can reach the best known solutions for 23 test functions and is able to successfully solve 21 test functions in all runs. Moreover, in this paper, a self-adaptive version of (µ + λ)-CDE is proposed which is the most competitive algorithm so far among the CEC2006 entries.

Novel one-dimensional organometallic half metals: vanadium-cyclopentadienyl, vanadium-cyclopentadienyl-benzene, and vanadium-anthracene wires.

By using the density functional theory, we find that organometallic multidecker sandwich clusters V(2 n+1)Cp(2 n+2), Vn(FeCp2)(n+1) (Cp=cyclopentadienyl), and V(2n)Ant(n+1) (Ant=anthracene) may have linear structures, and their total magnetic moments generally increase with the cluster size. The one-dimensional (VCp)infinity, (VBzVCp)infinity (Bz=benzene), and (V2Ant)infinity wires are predicted to be ferromagnetic half-metals, while the one-dimensional (VCpFeCp)infinity wire is a ferromagnetic semiconductor. The spin transportation calculations show that the finite V2(n+1)Cp2(n+2) and Vn(FeCp2)(n+1) sandwich clusters coupled to gold electrodes are nearly perfect spin-filters.

Embedded trellis coded quantization for JPEG2000.

A modified embedded trellis coded quantization (TCQ) for JPEG2000 is presented in this paper. The method for approximately inverting TCQ in the absence of the least significant bits is improved. Experimental results, presented using the optimal rate control algorithm and different embedded TCQ formulations, show that modified embedded TCQ yields significant performance improvement compared to the original one in JPEG2000.

Multiobjective optimization and hybrid evolutionary algorithm to solve constrained optimization problems.

This paper presents a novel evolutionary algorithm (EA) for constrained optimization problems, i.e., the hybrid constrained optimization EA (HCOEA). This algorithm effectively combines multiobjective optimization with global and local search models. In performing the global search, a niching genetic algorithm based on tournament selection is proposed. Also, HCOEA has adopted a parallel local search operator that implements a clustering partition of the population and multiparent crossover to generate the offspring population. Then, nondominated individuals in the offspring population are used to replace the dominated individuals in the parent population. Meanwhile, the best infeasible individual replacement scheme is devised for the purpose of rapidly guiding the population toward the feasible region of the search space. During the evolutionary process, the global search model effectively promotes high population diversity, and the local search model remarkably accelerates the convergence speed. HCOEA is tested on 13 well-known benchmark functions, and the experimental results suggest that it is more robust and efficient than other state-of-the-art algorithms from the literature in terms of the selected performance metrics, such as the best, median, mean, and worst objective function values and the standard deviations.

Visual modeling and simulation of adaptive immune system.

A visual modeling approach and its computational technique were proposed to represent and simulate a kind of adaptive immune system. Because adaptive immune system is comprised of immune cells and immune molecules etc, the hierarchical model of the immune system is proposed, more faithful and suitable for visual simulation than traditional models. The hierarchical immune system mainly consists of inherent immune tier, adaptive immune tier and immune cell tier. The tri-tier model of the immune system is seamless and coherent with the architecture of the artificial immune system. At last, the visual result of the simulation shows that the visual modeling approach can provide an effective way of understanding the adaptive immune system.