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.

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.

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.

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.

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.

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).

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.