Element dependence of enhancement in optics emission from laser-induced plasma under spatial confinement

In this study, the element dependence of spatial confinement effects in LIBS has been studied. Hemispheric cavities were used to confine laser-induced plasmas from aluminum samples with other trace elements. The enhancement factors were found to be dependent on the elements. Equations describing the element dependent enhancement factors were successfully deduced from the local thermodynamic equilibrium conditions, which have also been verified by the experimental results. Research results show that enhancement factors in LIBS with spatial confinement depend on the temperature, electron density, and compression ratio of plasmas, and vary with elements and atomic/ionic emission lines selected. Generally, emission lines with higher upper level energies have higher enhancement factors. Furthermore, with enhancement factor of a spectral line, temperatures and electron densities of plasmas known, enhancement factors of all the other elements in the plasmas could be estimated by the equations developed in this study

Immunomodulatory roles of metalloproteinases in rheumatoid arthritis

Rheumatoid arthritis (RA) is a chronic, autoimmune pathology characterized by persistent synovial inflammation and gradually advancing bone destruction. Matrix metalloproteinases (MMPs), as a family of zinc-containing enzymes, have been found to play an important role in degradation and remodeling of extracellular matrix (ECM). MMPs participate in processes of cell proliferation, migration, inflammation, and cell metabolism. A growing number of persons have paid attention to their function in inflammatory and immune diseases. In this review, the details of regulation of MMPs expression and its expression in RA are summarized. The role of MMPs in ECM remodeling, angiogenesis, oxidative and nitrosative stress, cell migration and invasion, cytokine and chemokine production, PANoptosis and bone destruction in RA disease are discussed. Additionally, the review summarizes clinical trials targeting MMPs in inflammatory disease and discusses the potential of MMP inhibition in the therapeutic context of RA. MMPs may serve as biomarkers for drug response, pathology stratification, and precision medicine to improve clinical management of rheumatoid arthritis

All laser direct writing process for temperature sensor based on graphene and silver

Abstract A highly sensitive temperature sensing array is prepared by all laser direct writing (LDW) method, using laser induced silver (LIS) as electrodes and laser induced graphene (LIG) as temperature sensing layer. A finite element analysis (FEA) photothermal model incorporating a phase transition mechanism is developed to investigate the relationship between laser parameters and LIG properties, providing guidance for laser processing parameters selection with laser power of 1–5 W and laser scanning speed (greater than 50 mm/s). The deviation of simulation and experimental data for widths and thickness of LIG are less than 5% and 9%, respectively. The electrical properties and temperature responsiveness of LIG are also studied. By changing the laser process parameters, the thickness of the LIG ablation grooves can be in the range of 30–120 μm and the resistivity of LIG can be regulated within the range of 0.031–67.2 Ω·m. The percentage temperature coefficient of resistance (TCR) is calculated as − 0.58%/°C. Furthermore, the FEA photothermal model is studied through experiments and simulations data regarding LIS, and the average deviation between experiment and simulation is less than 5%. The LIS sensing samples have a thickness of about 14 μm, an electrical resistivity of 0.0001–100 Ω·m is insensitive to temperature and pressure stimuli. Moreover, for a LIS-LIG based temperature sensing array, a correction factor is introduced to compensate for the LIG temperature sensing being disturbed by pressure stimuli, the temperature measurement difference is decreased from 11.2 to 2.6 °C, indicating good accuracy for temperature measurement. Graphical Abstrac

A Survey on Knee-Oriented Multiobjective Evolutionary Optimization

Yu G, Ma L, Jin Y, Du W, Liu Q, Zhang H. A Survey on Knee-Oriented Multiobjective Evolutionary Optimization. IEEE Transactions on Evolutionary Computation. 2022;26(6):1452-1472.Conventional multiobjective optimization algorithms (MOEAs) with or without preferences are successful in solving multi- and many-objective optimization problems. However, a strong hypothesis underlying their performance is that MOEAs are able to find a representative solution set to cover the entire Pareto-optimal front (PF) and decision makers are able to conveniently and precisely articulate their preference, which is not always easy to fulfill in practice. Accordingly, it is suggested that representative solutions in the naturally interesting regions of the PF rather than the whole PF should be targeted. A large body of research has been proposed to search or identify the knees or knee regions over the past decades. Therefore, this article aims to provide a comprehensive survey of the research on knee-oriented optimization. We start with a discussion of the importance and basic concepts of the knees, followed by a summary of knee-oriented benchmarks and indicators. After that, knee-oriented frameworks and techniques, and real-world applications are presented. Finally, potential challenges are pointed out and a few promising future lines of research are suggested. The survey offers a new perspective to develop MOEAs for solving multi- and many-objective optimization problems

Survey on Evolutionary Deep Learning: Principles, Algorithms, Applications, and Open Issues

Li N, Ma L, Yu G, Xue B, Zhang M, Jin Y. Survey on Evolutionary Deep Learning: Principles, Algorithms, Applications, and Open Issues. ACM Computing Surveys. 2024;56(2):1-34.Over recent years, there has been a rapid development of deep learning (DL) in both industry and academia fields. However, finding the optimal hyperparameters of a DL model often needs high computational cost and human expertise. To mitigate the above issue, evolutionary computation (EC) as a powerful heuristic search approach has shown significant merits in the automated design of DL models, so-called evolutionary deep learning (EDL). This article aims to analyze EDL from the perspective of automated machine learning (AutoML). Specifically, we first illuminate EDL from DL and EC and regard EDL as an optimization problem. According to the DL pipeline, we systematically introduce EDL methods ranging from data preparation, model generation, to model deployment with a new taxonomy (i.e., what and how to evolve/optimize), and focus on the discussions of solution representation and search paradigm in handling the optimization problem by EC. Finally, key applications, open issues, and potentially promising lines of future research are suggested. This survey has reviewed recent developments of EDL and offers insightful guidelines for the development of EDL

Roof Plane Segmentation from Airborne LiDAR Data Using Hierarchical Clustering and Boundary Relabeling

The roof plane segmentation is one of the key issues for constructing accurate three-dimensional building models from airborne light detection and ranging (LiDAR) data. Region growing is one of the most widely used methods to detect roof planes. It first selects one point or region as a seed, and then iteratively expands to neighboring points. However, region growing has two problems. The first problem is that it is hard to select the robust seed points. The other problem is that it is difficult to detect the accurate boundaries between two roof planes. In this paper, to solve these two problems, we propose a novel approach to segment the roof planes from airborne LiDAR point clouds using hierarchical clustering and boundary relabeling. For the first problem, we first extract the initial set of robust planar patches via an octree-based method, and then apply the hierarchical clustering method to iteratively merge the adjacent planar patches belonging to the same plane until the merging cost exceeds a predefined threshold. These merged planar patches are regarded as the robust seed patches for the next region growing. The coarse roof planes are generated by adding the non-planar points into the seed patches in sequence using region growing. However, the boundaries of coarse roof planes may be inaccurate. To solve this problem, namely, the second problem, we refine the boundaries between adjacent coarse planes by relabeling the boundary points. At last, we can effectively extract high-quality roof planes with smooth and accurate boundaries from airborne LiDAR data. We conducted our experiments on two datasets captured from Vaihingen and Wuhan using Leica ALS50 and Trimble Harrier 68i, respectively. The experimental results show that our proposed approach outperforms several representative approaches in both visual quality and quantitative metrics

Learning to optimize: Reference vector reinforcement learning adaption to constrained many-objective optimization of industrial copper burdening system

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The performance of decomposition-based algorithms is sensitive to the Pareto front shapes since their reference vectors preset in advance are not always adaptable to various problem characteristics with no a priori knowledge. For this issue, this paper proposes an adaptive reference vector reinforcement learning approach to decomposition-based algorithms for the industrial copper burdening optimization. The proposed approach involves two main operations, i.e., a reinforcement learning operation and a reference point sampling operation. Given the fact that the states of reference vectors interact with the landscape environment (quite often), the reinforcement learning operation treats the reference vector adaption process as a reinforcement learning task, where each reference vector learns from the environmental feedback and selects optimal actions for gradually fitting the problem characteristics. Accordingly, the reference point sampling operation uses estimation-of-distribution learning models to sample new reference points. Finally, the resultant algorithm is applied to handle the proposed industrial copper burdening problem. For this problem, an adaptive penalty function and a soft constraint-based relaxing approach are used to handle complex constraints. Experimental results on both benchmark problems and real-world instances verify the competitiveness and effectiveness of the proposed algorithm

<p>Time-resolved spectral-image laser-induced breakdown spectroscopy for precise qualitative and quantitative analysis of milk powder quality by fully excavating the matrix information</p>

A novel and effective method named time-resolved spectral-image laser-induced breakdown spectroscopy (TRSI-LIBS) was proposed to achieve precise qualitative and quantitative analysis of milk powder quality. To verify the feasibility of TRSI-LIBS, qualitative and quantitative analysis of milk powder quality was carried out. For qualitative analysis of foreign protein adulteration, the accuracy of models based on TRSI-LIBS was higher than those based on LIBS, with an accuracy improvement of about 5% to 10%. For the quantitative analysis of foreign protein adulteration and element content, the quantitative analysis models based on TSRI-LIBS also had better effect. For instance, limit of detection (LOD),determination coefficient of prediction (R(2)p), root-mean-square error of prediction (RMSEP) and average relative error of prediction (AREP) of quantitative model of calcium (Ca) content based on TRSI-LIBS improved from 1.47 mg/g, 0.95, 0.35 mg/g and 23.29% to 0.81 mg/g, 0.98, 0.20 mg/g and 12.60%

Pareto-Wise Ranking Classifier for Multi-Objective Evolutionary Neural Architecture Search

Ma L, Li N, Yu G, et al. Pareto-Wise Ranking Classifier for Multi-Objective Evolutionary Neural Architecture Search. IEEE Transactions on Evolutionary Computation. 2023:1-1.In multi-objective evolutionary neural architecture search (NAS), existing predictor-based methods commonly suffer from the rank disorder issue that a candidate high-performance architecture may have a poor ranking compared with the worse architecture in terms of the trained predictor.To alleviate the above issue, we aim to train a Pareto-wise end-to-end ranking classifier to simplify the architecture search process by transforming the complex multi-objective NAS task into a simple classification task. To this end, a classifier-based Pareto evolution approach is proposed, where an online classifier is trained to directly predict the dominance relationship between the candidate and reference architectures. Besides, an adaptive clustering method is designed to select reference architectures for the classifier, and an α-domination assisted approach is developed to address the imbalance issue of positive and negative samples. The proposed approach is compared with a number of state-of-the-art NAS methods on widely-used test datasets, and computation results show that the proposed approach is able to alleviate the rank disorder issue and outperforms other methods. Especially, the proposed method is able to find a set of promising network architectures with different model sizes ranging from 2M to 5M under diverse objectives and constraints

Decomposition-Based Multiobjective Optimization for Variable-Length Mixed-Variable Pareto Optimization and Its Application in Cloud Service Allocation

Ma L, Liu Y, Yu G, et al. Decomposition-Based Multiobjective Optimization for Variable-Length Mixed-Variable Pareto Optimization and Its Application in Cloud Service Allocation. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2023:1-14.In real-world applications, a specific class of multiobjective optimization problems, such as the cloud service allocation problem (CSAOPs), possess the characteristic of variable-length and mixed variables, termed as variable multiobjective optimization problems (VMMOPs). Unfortunately, little research has been reported to solve them. To fill the gap, we propose a tailored enhanced decomposition-based algorithm to handle the VMMOPs. Specifically, a variable-length coding structure is designed to flexibly represent the solutions of VMMOPs. In order to facilitate the solution generation, a simple dimensionality incremental learning strategy is developed to choose representative solutions for the training of two learning models. The one is the fast-clustering-based histogram model, which is built for the sampling of solutions in the continuous decision space, while the other one is the incremental learning-based histogram model, designed to sample solutions in discrete decision space. Following the traditional constructor of the DTLZ test suite and the features of CSAOPs, we present a test suite of VMMOPs for the verification of the performance of the methods in handling VMMOPs. Experimental results on a number of benchmark problems and two real CSAOPs have shown the effectiveness and competitiveness of the proposed method in handling VMMOPs