Niche Technique Research Articles

Specimen preparation for atom probe tomography

Abstract In the past 20 years or so, atom probe tomography has gone from a niche technique in physical metallurgy to a well-established method in many parts of materials science and beyond. This is owing to the improvement in both instrumentation and specimen preparation. Given the availability of instruments, successful specimen preparation is often the bottleneck in atom probe projects. In this article, the authors want to give an overview of the types of preparation techniques that are available and which challenges they pose. This includes basic electropolishing, which was dominant until the introduction of focused ion beam-based preparation some 25 years ago. Nowadays, focused ion beam-based preparation methods possibly represent the largest share, as they allow for site-specific specimen preparation, non-conductive specimens and often a higher throughput. They are continuously improved, i. e., through higher current plasma ion columns, cryo preparation for sensitive materials and room temperature liquids and ‘additive’ techniques for nanomaterials. This steadily increases the types of materials that can be analyzed using atom probe tomography.

An adaptive imbalance robust graph embedding broad learning system fault diagnosis for imbalanced batch processes data

Modern batch processes develop toward higher levels, and devices usually work at normal conditions with generally very few faults. Therefore, fewer fault data are collected than normal data, which makes the normal and fault modes imbalanced, and the fault diagnosis model is incapable focusing on the minority of fault samples as much as on most normal samples, consequently leading to insufficient generalization ability for the model. For that, we propose an adaptive imbalance-robust graph embedding broad learning system (AI-RGEBLS) in this paper. Firstly, it achieves adaptive correction of unbalanced samples by niche technique and synthetic minority over-sampling technique (SMOTE) with improved Mahalanobis distance. Then, broad learning system (BLS) is regularized by graph embedding, and further introduced the L2,1 norm constraint, which makes it possible to enhance the robustness of the model while considering the local manifold information of the data in the feature extraction process. Finally, the incremental learning approach is applied to the model to avoid the disastrous forgetting problem caused by training the whole model from zero. The effectiveness of the proposed method is verified by penicillin fermentation process and semiconductor etching process. It can effectively improve the speed of model training and provide better fault diagnosis for imbalanced batch processes compared with the existing methods.

Mechanochemistry: A Resurgent Force in Chemical Synthesis

Mechanochemistry, a solvent-free approach that harnesses mechanical energy, is emerging as a transformative technique in modern chemistry. It has emerged from a niche technique to a versatile tool with broad applications. By inducing physical and chemical transformations, it enables the synthesis of complex molecules and nanostructured materials. Recent advancements have extended its applications beyond simple physical transformations to encompass catalytic processes, unlocking new possibilities for selective synthesis and product design. This account delves into the fundamentals of mechanochemistry, its applications in organic synthesis, and also beyond traditional synthetic routes, mechanochemistry offers new avenues for molecular and materials discovery, expanding the scope of accessible chemical space.

Niche center identification differential evolution for multimodal optimization problems

Niching techniques are commonly incorporated into evolutionary computation (EC) algorithms to address multimodal optimization problems (MMOPs). Nevertheless, identifying proper individuals as niche centers remains the main challenge in niching techniques. Generally, niche centers should possess promising fitness (fitness aspect) and should be dispersedly distributed in different search regions (distance aspect). In this study, we propose a novel niching technique known as niche center identification (NCI) and integrate it with differential evolution (DE) for tackling MMOPs, termed NCIDE. In NCI, niche centers are first identified from both the fitness and distance aspects. Individuals that are not niche centers are added to their nearest niche centers to form niches. Moreover, we develop a niche-level archival-adaptive parameter scheme (NAAPS) to adaptively adjust the parameters at the niche level and reduce their sensitivity. Meanwhile, with the help of an archive, we can preserve the identified optima and reinitialize stagnant individuals for further exploration. The experimental results on the CEC2013 multimodal benchmark test suite demonstrate that NCIDE significantly outperforms several state-of-the-art multimodal algorithms, including multiple competition winners from CEC2015 and GECCO2017-GECCO2019. Finally, NCIDE is applied to solve multimodal nonlinear equation system (NES) problems to further illustrate its practical applicability.

Neutron Macromolecular Crystallography for Biological Samples—Current State and Future Perspectives

Knowledge of hydrogen locations and protonation states is critical for a fundamental understanding of biological macromolecular function/interactions, and neutron macromolecular crystallography (NMX) is uniquely suited among the experimental structural-determination methods to provide this information. However, despite its potential, NMX remains a relatively niche technique, due to substantial limitations. This review explores NMX’s role amongst the evolving landscape of structural biology, comparing and contrasting it to the historical gold standard of X-ray macromolecular crystallography (X-ray MX) and the increasingly prevalent electron-based methods—i.e., electron microscopy (EM) and electron diffraction (ED). Forthcoming developments (e.g., the European Spallation Source in Lund, Sweden, coming online) are expected to substantially address current limitations and ensure NMX will remain relevant in the coming decades.

Wireless sensor networks-based adaptive differential evolution for multimodal optimization problems

In wireless sensor networks (WSN), we often detect the monitoring areas among different sensors so that the sensors can be switched on and off adaptively to save energy and extend their lifetime. Inspired by the principle of WSN, a WSN-based adaptive differential evolution (WSNADE) algorithm is proposed in this paper, together with a WSN-based adaptive niching technique (WANT) and two novel strategies called protection-based dual-scale mutation (PDM) strategy and multi-level reset (MLR) strategy, for solving multimodal optimization problems (MMOPs). In WANT, each individual is considered as a sensor with its monitoring area. If the monitoring areas of two individuals intersect, which means these two individuals monitor the similar area and should be partitioned into the same niche. In this way, WANT can adaptively form a niche for each individual, avoiding the sensitivity of niching parameters. Based on WANT, the PDM strategy is designed to select the appropriate mutation strategy for each individual. Besides, to save fitness evaluations (FEs) for exploring more promising areas, the MLR strategy is developed to store the promising individuals and reset the stagnant individuals. The experimental results on 20 multimodal benchmark test functions in CEC2015 multimodal competition show that the proposed WSNADE algorithm generally performs better than or at least comparable with other state-of-the-art multimodal algorithms, including the winner of the CEC2015 competition. Finally, WSNADE is applied to a real-world multimodal application in multiple competitive facilities location design (MCFLD) problem to illustrate its practical applicability.

A new nested-niche technique for optimization design of composite laminated structure

Optimization design of composite laminated structure has typical features as multimodality and variable-dimension when the ply number is considered as a design variable, the niche technique combined with evolutionary algorithm is an effective method for such problem. In order to get lighter weight, a new nested-niche technique that can consider the diversity of the population in the ply number and the ply orientation successively is proposed in this paper to improve the optimization performance. Performance of the proposed technique is verified by engineering cases, the weight obtained based on the nested-niche technique is 3.32kg, reduced by 25.2% and 11.5% compared with GA and INDE in references. Finally, influence of the inner-niche radius of the technique on the performance is discussed.

A genetic programming-based method for image classification with small training data

Genetic programming (GP) has been considerably used for image classification because of its ability to learn simple and effective models. However, most GP methods require a large amount of training data to learn informative features for classification, where the generalization performance might be poor when only a few training instances are available. In addition to using classification accuracy to assess the goodness of GP individuals/solutions like in most GP methods, this paper proposes a new fitness function containing distance measures. The proposed method uses different distance measures to deal with binary and multi-class classification automatically. By simultaneously minimizing the within-class distance and maximizing the between-class distance, the generalization performance can be improved. Furthermore, existing GP methods typically employ standard crossover to search for the best individuals from the whole search space. However, these methods might not completely exploit the potential local search space. Based on the niching technique, this paper develops a new crossover operator, which enables better exploitation of the global and local search space, improving learning effectiveness and classification accuracy. The new approach achieves significantly better generalization performance than almost all benchmark methods on eight datasets and is also computationally efficient. Further analysis demonstrates the significance of the new fitness function and crossover operator and shows the potentially good interpretability of the learned models.

Dynamic niching particle swarm optimization with an external archive-guided mechanism for multimodal multi-objective optimization

Multimodal multi-objective optimization problems (MMOPs) contain multiple equivalent Pareto optimal sets (PSs) corresponding to the same Pareto front (PF). However, simultaneously locating well-distributed and well-converged multiple equivalent global PSs and PF remains challenging. Therefore, this paper proposes dynamic niching particle swarm optimization (PSO) with an external archive-guided (AG) mechanism, termed DNPSO-AG, for solving MMOPs. In DNPSO-AG, a clustering-based dynamic niching technique is integrated with PSO to divide the population into multiple niches. In addition, a leader updating method controls the updating of the leaders. Furthermore, a novel external archive-guided mechanism guides the evolution of multiple niches and enhances the distribution of solutions, which comprises two strategies: the adaptive division of the external archive strategy, which adaptively divides the external archive into multiple sub-archives, and the distance-based sub-archive and niche matching strategy, which assigns sub-archives to multiple niches for maintenance. The experimental results demonstrate that the proposed DNPSO-AG outperforms seven other state-of-the-art competitors on the CEC 2019 MMOP test suite in terms of the inverted generational distance (IGD) and IGD in the decision space (IGDX) metrics, with improvements of 21.3% and 9.1% over the best-performing competitor, respectively.

A multimodal multi-objective evolutionary algorithm with two-stage dual-indicator selection strategy

Multimodal multi-objective problems (MMOPs) arise frequently in the real world, in which multiple Pareto optimal solution sets correspond to the same point on the Pareto front. To find more equivalent Pareto sets, numerous multimodal multi-objective evolutionary algorithms (MMEAs) based on niching have been developed. However, traditional niching methods have parameter sensitivity issues. Moreover, most existing MMEAs always perform diversity maintenance operations in the decision space and the objective space sequentially based on preferences, without considering a diversity maintenance operation that takes into account both the decision space and the objective space simultaneously. To tackle these challenges, a multimodal multi-objective evolutionary algorithm with two-stage double indicator selection strategy (MMEA-TDI) is developed. First, a dual-indicator with adaptive niche radius is developed to estimate the crowding status of the population. Then, a diversity-based mating selection is suggested to select well-distributed parents for mating. Furthermore, the environmental selection is performed in two stages. In the first stage, a parameter-free automatic niching technique based on clustering is adopted to well balance diversity and convergence in the decision space, while a double indicator selection strategy is performed for maintaining diversity in both decision and objective spaces in the second stage. To assess the performance of the proposed algorithm, extensive experiments are conducted on the well-known benchmark functions, in comparison with eight state-of-the-art MMEAs. Experimental results demonstrate that the proposed algorithm is significantly superior to the competing algorithms.

Differential evolution with nearest better clustering for multimodal multiobjective optimization

The main aim of multimodal multiobjective optimization algorithms is to find multiple optimal solutions for multiple objectives. The problem with multiple objectives is to maintain the diversity and convergence balance in both decision and objective space. Better performance in one space can lead to deterioration of performance in another space. To effectively perform exploration and exploitation in the decision space, we have proposed a niching technique in our proposed algorithm based on differential evolution to form groups of the population. The niching technique also incorporates a balancing strategy to average the size of niches and a generation strategy to add new elements by finding promising areas. We have also proposed an archive strategy to store the possible optimal solutions that help to explore the decision space and ensure diversity as well as the convergence of the solutions. To show the efficiency of our proposed algorithm, we have performed exhaustive experimentation on the benchmark test suite and compared the results with state-of-the-art algorithms. The experimental results obtained using the proposed algorithm highlight the efficiency and effectiveness of our proposed algorithm in both decision and objective space.

Interactive niching-based two-stage evolutionary algorithm for constrained multiobjective optimization

When solving constrained multiobjective optimization problems (CMOPs), how to maintain diversity without losing convergence is a major challenge, because some small discrete feasible regions make the population hard to find a complete feasible Pareto Front. To this end, an interactive niching-based two-stage evolutionary algorithm for constrained multiobjective optimization, named INCMO, is proposed in this study. At each stage of INCMO, both two populations are employed to focus on different evolution purposes. Specifically, the main population optimizes the original CMOP, while the auxiliary population only considers the objectives by ignoring all constraints. INCMO uses two different niching mechanisms in two stages. In the early stage, each population independently uses the niching technique to generate offspring, to maintain diversity. In addition, two offspring populations are shared to realize migration between two populations. In the later stage, two populations cooperate to generate offspring based on an interactive niching technique. To be specific, two populations are merged and then divided into different niches. In this case, individuals from two populations will be assigned to the same niche. After that, individuals in the niche produce offspring through crossover and mutation. The process of generating offspring also promotes more effective migration between the two populations on the basis of increasing the diversity of population. Compared with other well-established methods, the proposed method in this study shows better performance on four test suites including 47 benchmark problems.

An overview of the optical characterization of free microparticles and their radiative properties

We discuss how the range of information available from microparticle analysis can be extended in a number of applications by measuring a specific set of optical properties of individual particles using light scattering. Central to these measurements are the real and imaginary components of the forward scattered field, the former being equal to the extinction cross-section except for a few particle-independent constants. Although still a niche technique, it has some inherent advantages and great potential for particle characterization, especially in the challenging near-wavelength size range. A selection of cases is covered from an experimental point of view, while some essential models are introduced to illustrate the underlying physical phenomena. We present a benchmark of experimental results from the literature and other examples that support optical diagnostics applied in science and industrial processes. As a key point of this work, we show that by accessing the fundamental properties of scatterers the inversion of light scattering data can be avoided. This provides model-independent results closer to application requirements without the drawbacks of case-specific assumptions.

A ring-hierarchy-based evolutionary algorithm for multimodal multi-objective optimization

Multimodal multi-objective optimization problems (MMOPs) involve multiple equivalent Pareto sets (PSs) with identical Pareto front (PF). Popular multimodal multi-objective evolutionary algorithms (MMEAs) are capable of finding multiple equivalent PSs. However, most of MMEAs lead to imbalanced or local PSs are dominated and lost when tackling several MMOPs with the imbalance between convergence and diversity (MMOP-ICD) or MMOP with local Pareto solutions (MMOPL). To tackle this issue, we propose a ring-hierarchy-based evolutionary algorithm for multimodal multi-objective optimization. A ring-based niche technique is used based on the Pareto-based ranking hierarchy. Each hierarchy and its upper and lower neighbors hierarchy form a ring-hierarchy topology structure. Subsequently, a local convergence quality that considers the dominance relationship and objective values between all individuals is involved in the ring-hierarchy-based evolutionary strategy. It updates individuals and improves the population convergence quality. Moreover, a distance-based dominance selection that considers the distance between the neighbors and the dominance relationship is also developed. In this case, some individuals that approach imbalanced PS and local PS are maintained in the population instead of being dominated. Meanwhile, a dual-crowding distance is also involved in distance-based dominance selection to select diverse individuals. The proposed algorithm and several state-of-the-art MMEAs are tested on several MMOPs benchmarks. The experimental results demonstrate that the proposed algorithm is competitive and is capable of locating imbalanced PSs and local PSs.

Path tracking control method for automatic navigation rice transplanters based on VUFC and improved BAS algorithm

AbstractDuring the operation of automatic navigation rice transplanter, the accuracy of path tracking is influenced by whether the transplanter can enter the stable state of linear path tracking quickly, thus affecting the operation quality and efficiency. To reduce the time to enter the path tracking stable state and improve the tracking accuracy and stability for the rice transplanter, path tracking control method based on variable universe fuzzy control (VUFC) and improved beetle antenna search (BAS) is proposed in this paper. VUFC is applied to achieve adaptive adjustment of the fuzzy universe by dynamically adjusting the quantization and scaling factors according to the variations of errors by the contraction–expansion factor. To solve the problem of setting the contraction–expansion factor in VUFC and real-time performance, an offline parameter optimization method is presented to calculate the optimal contraction–expansion factor by an iterative optimization algorithm in a path tracking simulation model, where the iterative optimization algorithm is the BAS algorithm improved by the isolated niching technique and adaptive step size strategy in this paper. To verify the effectiveness of the proposed path tracking control method, simulation and field linear path tracking experiments were carried out. Experimental results indicate that the proposed method reduces the time of entering the stable state of linear path tracking and improves the accuracy and stability of path tracking compared with the pure pursuit control method.

Multi-population cooperative teaching–learning-based optimization for nonlinear equation systems

Solving nonlinear equation systems (NESs) requires locating different roots in one run. To effectively deal with NESs, a multi-population cooperative teaching–learning-based optimization, named MCTLBO, is presented. The innovations of MCTLBO are as follows: (i) two niching technique (crowding and improved speciation) are integrated into the algorithm to enhance population diversity; (ii) an adaptive selection scheme is proposed to select the learning rules in the teaching phase; (iii) the new learning rules based on experience learning are developed to promote the search efficiency in the teaching and learning phases. MCTLBO was tested on 30 classical problems and the experimental results show that MCTLBO has better root finding performance than other algorithms. In addition, MCTLBO achieves competitive results in eighteen new test sets.

Differential evolution with nearest density clustering for multimodal optimization problems

Multimodal optimization problems (MMOPs) refer to problems with multiple optimal solutions in a given search region. Evolutionary algorithms (EAs) are widely used to search for optimal solutions. To address the multimodal problem, we propose differential evolution with nearest density clustering (NDC-DE), which combines a density clustering-based technique and a differential evolutionary algorithm. NDC-DE includes three mechanisms. First, we use nearest density clustering (NDC) to divide the initial population into multiple subpopulations and identify the best individuals in each ecological niche. Combining niche techniques with evolutionary algorithms significantly improve their ability to solve MMOPs. Clustering algorithms are useful niche techniques divide the population into subpopulations based on different characteristics. Second, we propose two new mutation operators based on nbest. Last, an adaptive species redistribution mechanism based on the opposition-based learning (OBL) mechanism is proposed during the evolutionary iteration of NDC-DE to enhance the diversity of niches. We compare NDC-DE with 15 other algorithms on the CEC2013 benchmark function and show that it outperforms these algorithms in handling MMOPs.

SFC and CE—A Comparison of Two Orthogonal Methods for the Analysis of Dihydrochalcones in Apple Leaves

In recent years the analysis of natural products has been carried out using a range of approaches, but mainly utilizing liquid chromatography (LC) or gas chromatography (GC). However, alternative approaches with orthogonal selectivity like capillary electrophoresis (CE) and supercritical fluid chromatography (SFC) have increasingly been employed as well, even though they are often considered niche techniques only. In this study, we intended to confirm and compare their suitability as reliable state-of-the-art methods for the analysis of bioactive compounds by developing CE and SFC for the analysis of dihydrochalcones (DHCs) in apple leaves. The analytes were chosen as they have shown interesting pharmacological effects, such as anti-inflammatory, anti-tumor and immunomodulatory activities, and also present an interesting analytical challenge due to their structural similarity and polarity range. Both methods were well capable to separate the five standard compounds within short separation times and fulfilling the demands for an environmentally friendly “green” technology. CE as well as the SFC assay were validated for linearity, sensitivity, accuracy and precision according to ICH guidelines and met all respective requirements. Using the optimized methods, several Malus sp. samples were analyzed whereby a significant difference in the qualitative as well as quantitative DHC profile was revealed, with overall DHC concentrations ranging from 5.47% to 17.24%.

Feature subset selection using multimodal multiobjective differential evolution

The main aim of feature subset selection is to find the minimum number of required features to perform classification without affecting the accuracy. It is one of the useful real-world applications for different types of classification datasets. Different feature subsets may achieve similar classification accuracy, which can help the user to select the optimal features. There are two main objectives involved in selecting a feature subset: minimizing the number of features and maximizing the accuracy. However, most of the existing studies do not consider multiple feature subsets of the same size. In this paper, we have proposed an algorithm for multimodal multiobjective optimization based on differential evolution with respect to the feature subset selection problem. We have proposed the probability initialization method to identify the selected features with equal distribution in the search space. We have also proposed a niching technique to explore the search space and exploit the nearby solutions. Further, we have proposed a convergence archive to locate and store the optimal feature subsets. Exhaustive experimentation has been conducted on different datasets with varying characteristics to identify multiple feature subsets. We have also proposed an evaluation metric for the quantitative comparison of the proposed algorithm with the existing algorithms. Results have also been compared with existing algorithms in the objective space and in terms of classification accuracy, which shows the effectiveness of the proposed algorithm.

State-of-the-art mass spectrometry imaging applications in biomedical research.

Mass spectrometry imaging has advanced from a niche technique to a widely applied spatial biology tool operating at the forefront of numerous fields, most notably making a significant impact in biomedical pharmacological research. The growth of the field has gone hand in hand with an increase in publications and usage of the technique by new laboratories, and consequently this has led to a shift from general MSI reviews to topic-specific reviews. Given this development, we see the need to recapitulate the strengths of MSI by providing a more holistic overview of state-of-the-art MSI studies to provide the new generation of researchers with an up-to-date reference framework. Here we review scientific advances for the six largest biomedical fields of MSI application (oncology, pharmacology, neurology, cardiovascular diseases, endocrinology, and rheumatology). These publications thereby give examples for at least one of the following categories: they provide novel mechanistic insights, use an exceptionally large cohort size, establish a workflow that has the potential to become a high-impact methodology, or are highly cited in their field. We finally have a look into new emerging fields and trends in MSI (immunology, microbiology, infectious diseases, and aging), as applied MSI is continuously broadening as a result of technological breakthroughs.