Types Of Features Research Articles

Composites with Immobilized Bioactive Spirulina on an Inorganic Substrate (Yellow Clay, Hydroxyapatite, SiO2, TiO2, ZnO).

In order to improve the structural properties of clays and composites of powdered spirulina, clay, nanosilica, hydroxyapatite, TiO2 and ZnO were used as an additive for mechanical processing. As a result, composites with natural nanostructured materials (NNM) are prepared with improved structural properties and bioactivity. The mixtures based on NNM with crystalline kaolinite, clays and admixtures were processed in a knife mill. The materials were characterized using FTIR spectroscopy, nitrogen adsorption and desorption, SEM release of bioactive components (anthocyanin 0,004-0,07 mg/g; chlorophyll 20-29 mg/g), composite toxicity level (below 25%), particle size measurement and surface charge density, zeta potential. Adsorption enthalpies during the formation of an intermolecular complex during the interactions of an anthocyanin molecule with the appropriate component of the composite were also calculated. There are regularities in the characteristics depending on the type of NNM, particle morphology and textural features of solids. The morphological and structural properties of the components changed slighty in the blends because the processing was conducted under relatively low mechanical stress. The morphological, textural and structural characteristics of the composites as well as the transformation to a nanostructured state, assume great bioactive activity of the composites, interesting for practical applications in medicine and cosmetics.

Analysis of EEG Fluctuation Patterns Using Nonlinear Phase-Based Functional Connectivity Measures for Emotion Recognition

Automated emotion recognition is crucial in identifying and monitoring psychological disorders. Although several electroencephalography (EEG)-based methods have been explored for emotion recognition, capturing the subtle oscillations within EEG signals associated with distinct emotional states remains a persistent challenge. Nonlinear phase-based functional connectivity (FC) can capture the intricate time-varying patterns of brain activity during the processing of emotions. In this work, an attempt has been made to characterize the EEG-based emotional states using nonlinear phase-based FC techniques. For this, the EEG signals are obtained from the publicly available DEAP database and decomposed into four frequency bands: Theta (4–7[Formula: see text]Hz), alpha (8–12[Formula: see text]Hz), beta (13–30[Formula: see text]Hz) and gamma (30–45[Formula: see text]Hz). Three nonlinear phase-based FC measures, namely phase lag index (PLI), phase locking value (PLV) and Rho index, are extracted from individual frequency bands. Two types of features, namely network features and FC indices, are fed to three classifiers, namely random forest (RF), extreme gradient boosting (XGB) and K-Nearest Neighbors (KNN). The results reveal that the proposed approach can capture EEG dynamics to characterize emotional states. The gamma band-based Rho index demonstrated prominence in discriminating arousal and valence emotional states. The utilization of the Rho index-based FC feature effectively reveals interactions among cortical brain regions in response to audio-visual stimuli. Thus, the proposed approach could be extended to classifying various emotional states in clinical settings.

Machine Learning Techniques for Predicting Drug-Related Side Effects: A Scoping Review.

Drug safety relies on advanced methods for timely and accurate prediction of side effects. To tackle this requirement, this scoping review examines machine-learning approaches for predicting drug-related side effects with a particular focus on chemical, biological, and phenotypical features. This was a scoping review in which a comprehensive search was conducted in various databases from 1 January 2013 to 31 December 2023. The results showed the widespread use of Random Forest, k-nearest neighbor, and support vector machine algorithms. Ensemble methods, particularly random forest, emphasized the significance of integrating chemical and biological features in predicting drug-related side effects. This review article emphasized the significance of considering a variety of features, datasets, and machine learning algorithms for predicting drug-related side effects. Ensemble methods and Random Forest showed the best performance and combining chemical and biological features improved prediction. The results suggested that machine learning techniques have some potential to improve drug development and trials. Future work should focus on specific feature types, selection techniques, and graph-based methods for even better prediction.

Aquaculture Ponds Identification Based on Multi-Feature Combination Strategy and Machine Learning from Landsat-5/8 in a Typical Inland Lake of China

Inland aquaculture ponds, as an important land use type, have brought great economic benefits to local people but at the same time have caused many environmental problems threatening regional ecology security. Therefore, understanding the spatiotemporal pattern of aquaculture ponds and its potential influence on water quality is vital for the sustainable development of inland lakes. In this study, based on Landsat5/8 images, three types of land features, namely spectral features, index features, and texture features, and five machine learning algorithms, namely random forest (RF), extreme gradient boosting (XGBoost), artificial neural network (ANN), k-nearest neighbor (KNN), and Gaussian naive Bayes (GNB), were combined to identify aquaculture ponds and some other primary land use types around a typical inland lake of China. The results demonstrated that the XGBoost algorithm that integrated the three features performed the best among all groups of the five machine learning algorithms and the three features, with an overall accuracy of up to 96.15%. In particular, the texture features provided additional useful information besides the spectral features to allow more accurately separation of aquaculture ponds from other land use types and thus improve the land use mapping ability in complex inland lakes. Next, this study examined the tendency of aquaculture ponds and found a segmented increase mode, namely sharp increase during 1984–2003 and then slow elevation since 2003. Further positive correlation detected between the area of aquaculture ponds and the phytoplankton population dynamics suggest a likely influence of aquaculture activity on the lake water quality. This study provides an important scientific basis for the sustainable management and ecological protection of inland lakes.

Digital voice-based biomarker for monitoring respiratory quality of life: findings from the colive voice study

Regular monitoring of respiratory quality of life (RQoL) is essential in respiratory healthcare, facilitating prompt diagnosis and tailored treatment for chronic respiratory diseases. Voice alterations resulting from respiratory conditions create unique audio signatures that can potentially be utilized for disease screening or monitoring. Analyzing data from 1908 participants from the Colive Voice study, which collects standardized voice recordings alongside comprehensive demographic, epidemiological, and patient-reported outcome data, we evaluated various strategies to estimate RQoL from voice, including handcrafted acoustic features, standard acoustic feature sets, and advanced deep audio embeddings derived from pretrained convolutional neural networks. We compared models using clinical features alone, voice features alone, and a combination of both. The multimodal model combining clinical and voice features demonstrated the best performance, achieving an accuracy of 70.8% and an area under the receiver operating characteristic curve (AUROC) of 0.77; an improvement of over 5% in terms of accuracy and 7% in terms of AUROC compared to model utilizing voice features alone. Incorporating vocal biomarkers significantly enhanced the predictive capacity of clinical variables across all acoustic feature types, with a net classification improvement (NRI) of up to 0.19. Our digital voice-based biomarker is capable of accurately predicting RQoL, either as an alternative to or in conjunction with clinical measures, and could be used to facilitate rapid screening and remote monitoring of respiratory health status.

Optimalisasi Pembelajaran Melalui Pelatihan Pembuatan Media Classpoint bagi Guru Sekolah Dasar

This community service aims to provide guidance to teachers to optimize learning by creating learning media through the Classpoint application. The implementation of this community service activity is done face-to-face (offline), while adhering to applicable health protocols. The method of implementing community service includes problem identification, presentation by the team of lecturers, question and answer sessions and discussions, as well as direct assistance in the process of making interactive Classpoint media. The media developed through the Classpoint application is expected to motivate students to learn and increase their participation in building knowledge. Training at State Elementary School 14 Cakranegara went smoothly with the assistance of 5 members of the community service team and 2 students who guided 10 participants, thus enabling them to overcome obstacles quickly. The speakers were also highly communicative, providing step-by-step guidance, and ensuring that each participant understood the steps before moving on to the next stage. During the training, the five types of Classpoint application features, such as multiple choice, word cloud, short answer, slide description, and image upload, could be effectively applied.

Predicting Winter Wheat Yield with Dual-Year Spectral Fusion, Bayesian Wisdom, and Cross-Environmental Validation

Winter wheat is an important grain that plays a crucial role in agricultural production and ensuring food security. Its yield directly impacts the stability and security of the global food supply. The accurate monitoring of grain yield is imperative for precise agricultural management. This study aimed to enhance winter wheat yield predictions with UAV remote sensing and investigate its predictive capability across diverse environments. In this study, RGB and multispectral (MS) data were collected on 6 May 2020 and 10 May 2022 during the grain filling stage of winter wheat. Using the Pearson correlation coefficient method, we identified 34 MS features strongly correlated with yield. Additionally, we identified 24 texture features constructed from three bands of RGB images and a plant height feature, making a total of 59 features. We used seven machine learning algorithms (Cubist, Gaussian process (GP), Gradient Boosting Machine (GBM), Generalized Linear Model (GLM), K-Nearest Neighbors algorithm (KNN), Support Vector Machine (SVM), Random Forest (RF)) and applied recursive feature elimination (RFE) to nine feature types. These included single-sensor features, fused sensor features, single-year data, and fused year data. This process yielded diverse feature combinations, leading to the creation of seven distinct yield prediction models. These individual machine learning models were then amalgamated to formulate a Bayesian Model Averaging (BMA) model. The findings revealed that the Cubist model, based on the 2020 and 2022 dataset, achieved the highest R2 at 0.715. Notably, models incorporating both RGB and MS features outperformed those relying solely on either RGB or MS features. The BMA model surpassed individual machine learning models, exhibiting the highest accuracy (R2 = 0.725, RMSE = 0.814 t·ha−1, MSE = 0.663 t·ha−1). Additionally, models were developed using one year’s data for training and another year’s data for validation. Cubist and GLM stood out among the seven individual models, delivering strong predictive performance. The BMA model, combining these models, achieved the highest R2 of 0.673. This highlights the BMA model’s ability to generalize for multi-year data prediction.

Strengthening LLM ecosystem security: Preventing mobile malware from manipulating LLM-based applications

Large language model (LLM) platform vendors have begun to make their models available for developers to build for different use cases. However, the emergence of LLM-based applications may raise security and privacy issues, and even LLM-based applications may be susceptible to malware. To strengthen LLM ecosystem security, it's crucial to develop malware detection algorithms for various platforms. We pay attention to Android malware because the Android platform is widely used and vulnerable. Existing single feature based-solutions cannot effectively describe applications, and aged models fail to detect new malware as Android platform develops and malware evolves. Therefore, existing detection methods are ill-suited for evolved malware that may manipulate LLM-based applications. To tackle the above problems, we design EvolveDroid, an anti-aging Android malware detection system. On the one hand, EvolveDroid utilizes different view features to reflect malware behavior from multiple dimensions, and maximizes the advantages of each feature type through feature aggregation. On the other hand, EvolveDroid learns good representation of applications through contrastive learning and generates pseudo labels by measuring the distance between unknown samples and existing samples for model updating. Extensive evaluations show that EvolveDroid outperforms state-of-the-art (sota) solutions in detection performance and slowing model aging.

Deep learning for enhanced index tracking

We develop a novel deep learning method for the enhanced index tracking problem, which aims to outperform an index while effectively controlling the tracking error. We generate a dynamic trading policy from a neural network that accepts a set of features as inputs. We design four blocks in the neural network architecture to handle different types of features, including regimes of the index and stocks, their short-term characteristics, and the current allocation. Outputs from the blocks are integrated into the final output that changes the portfolio allocation. We test our model on several indexes in empirical studies based on real market data. Out-of-sample results reveal the importance of different features and demonstrate the ability of our method in obtaining excess returns while effectively controlling the tracking error, downside risk, and transaction costs.

Does consensus contour improve robustness and accuracy in 18F-FDG PET radiomic features?

PurposeThe purpose of our study is to validate the robustness and accuracy of consensus contour in 2-deoxy-2-[18\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{18}$$\\end{document}F]fluoro-D-glucose (18\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{18}$$\\end{document}F-FDG) PET radiomic features.Methods225 nasopharyngeal carcinoma (NPC) and 13 extended cardio-torso (XCAT) simulated data were enrolled. All segmentation were performed with four segmentation methods under two different initial masks, respectively. Consensus contour (ConSeg) was then developed using the majority vote rule. 107 radiomic features were extracted by Pyradiomics based on segmentation and the intraclass correlation coefficient (ICC) was calculated for each feature between masks or among segmentation, respectively. In XCAT ICC between segmentation and simulated ground truth were also calculated to access the accuracy.ResultsICC varied with the dataset, segmentation method, initial mask and feature type. ConSeg presented higher ICC for radiomic features in robustness tests and similar ICC in accuracy tests, compared with the average of four segmentation results. Higher ICC were also generally observed in irregular initial masks compared with rectangular masks in both robustness and accuracy tests. Furthermore, 19 features (17.76%) had ICC ≥ 0.75 in both robustness and accuracy tests for any of the segmentation methods or initial masks. The dataset was observed to have a large impact on the correlation relationships between radiomic features, but not the segmentation method or initial mask.ConclusionsThe consensus contour combined with irregular initial mask could improve the robustness and accuracy in radiomic analysis to some extent. The correlation relationships between radiomic features and feature clusters largely depended on the dataset, but not segmentation method or initial mask.

The influence of geometric boundary features on droplet wetting and directional motion

Structured surfaces with micro and nanostructures have shown great potential for a wide range of applications, such as self-cleaning, deicing, and drag reduction. Previous research has extensively explored the interactions between droplets and microstructures, particularly in terms of Cassie-Baxter and Wenzel states. However, there is still a notable gap in systematically studying the interactions between droplets and various types of geometrical boundary features. In this study, we employed molecular dynamics simulations to investigate how different geometric boundary features affect the wetting behavior and motion of water droplets. Our detailed analysis led to the derivation of a formula to calculate the critical contact angle for a droplet interacting with any geometric boundary feature from a mechanical perspective. Based on these findings, we successfully controlled motion direction of water droplets along a separation membrane. We observed that due to the influence of geometric boundary features at the holes, droplets exhibited unidirectional motion on the separation membrane. By considering the influence of geometric boundary features, our research provides valuable insights for preventing wetting transition, enhancing the storage of lubricating oil in grooves, facilitating efficient oil–water separation, and regulating fluid flow in microchannels.

A novel model for relation prediction in knowledge graphs exploiting semantic and structural feature integration

Relation prediction is a critical task in knowledge graph completion and associated downstream tasks that rely on knowledge representation. Previous studies indicate that both structural features and semantic information are meaningful for predicting missing relations in knowledge graphs. This has led to the development of two types of methods: structure-based methods and semantics-based methods. Since these two approaches represent two distinct learning paradigms, it is difficult to fully utilize both sets of features within a single learning model, especially deep features. As a result, existing studies usually focus on only one type of feature. This leads to an insufficient representation of knowledge in current methods and makes them prone to overlooking certain patterns when predicting missing relations. In this study, we introduce a novel model, RP-ISS, which combines deep semantic and structural features for relation prediction. The RP-ISS model utilizes a two-part architecture, with the first component being a RoBERTa module that is responsible for extracting semantic features from entity nodes. The second part of the system employs an edge-based relational message-passing network designed to capture and interpret structural information within the data. To alleviate the computational burden of the message-passing network on the RoBERTa module during the sampling process, RP-ISS introduces a node embedding memory bank, which updates asynchronously to circumvent excessive computation. The model was assessed on three publicly accessible datasets (WN18RR, WN18, and FB15k-237), and the results revealed that RP-ISS surpasses all baseline methods across all evaluation metrics. Moreover, RP-ISS showcases robust performance in graph inductive learning.

Test Input Prioritization for 3D Point Clouds

3D point cloud applications have become increasingly prevalent in diverse domains, showcasing their efficacy in various software systems. However, testing such applications presents unique challenges due to the high-dimensional nature of 3D point cloud data and the vast number of possible test cases. Test input prioritization has emerged as a promising approach to enhance testing efficiency by prioritizing potentially misclassified test cases during the early stages of the testing process. Consequently, this enables the early labeling of critical inputs, leading to a reduction in the overall labeling cost. However, applying existing prioritization methods to 3D point cloud data is constrained by several factors: (1) inadequate consideration of crucial spatial information, and (2) susceptibility to noises inherent in 3D point cloud data. In this article, we propose PCPrior , the first test prioritization approach specifically designed for 3D point cloud test cases. The fundamental concept behind PCPrior is that test inputs closer to the decision boundary of the model are more likely to be predicted incorrectly. To capture the spatial relationship between a point cloud test and the decision boundary, we propose transforming each test (a point cloud) into a low-dimensional feature vector, toward indirectly revealing the underlying proximity between a test and the decision boundary. To achieve this, we carefully design a group of feature generation strategies, and for each test input, we generate four distinct types of features, namely spatial features, mutation features, prediction features, and uncertainty features. Through a concatenation of the four feature types, PCPrior assembles a final feature vector for each test. Subsequently, a ranking model is employed to estimate the probability of misclassification for each test based on its feature vector. Finally, PCPrior ranks all tests based on their misclassification probabilities. We conducted an extensive study based on 165 subjects to evaluate the performance of PCPrior, encompassing both natural and noisy datasets. The results demonstrate that PCPrior outperforms all of the compared test prioritization approaches, with an average improvement of 10.99% to 66.94% on natural datasets and 16.62% to 53% on noisy datasets.

Weed Detection and Classification with Computer Vision Using a Limited Image Dataset

In agriculture, as precision farming increasingly employs robots to monitor crops, the use of weeding and harvesting robots is expanding the need for computer vision. Currently, most researchers and companies address these computer vision tasks with CNN-based deep learning. This technology requires large datasets of plant and weed images labeled by experts, as well as substantial computational resources. However, traditional feature-based approaches to computer vision can extract meaningful parameters and achieve comparably good classification results with only a tenth of the dataset size. This study presents these methods and seeks to determine the minimum number of training images required to achieve reliable classification. We tested the classification results with 5, 10, 20, 40, 80, and 160 images per weed type in a four-class classification system. We extracted shape features, distance transformation features, color histograms, and texture features. Each type of feature was tested individually and in various combinations to determine the best results. Using six types of classifiers, we achieved a 94.56% recall rate with 160 images per weed. Better results were obtained with more training images and a greater variety of features.

Progressive prediction: Video anomaly detection via multi‐grained prediction

AbstractVideo Anomaly Detection (VAD) has been an active research field for several decades. However, most existing approaches merely extract a single type of feature from videos and define a single paradigm to indicate the extent of abnormalities. A coarse‐to‐fine three‐level prediction is built by integrating different levels of spatio‐temporal representations, better highlighting the difference between normal and abnormal behaviors. First, an object‐level trajectory prediction is proposed to model human historical position using a graph transformer network. Subsequently, skeleton‐level prediction is achieved by incorporating the positional information from the trajectory prediction. More importantly, based on the predicted skeleton, a skeleton‐guided pixel‐level region prediction is performed. A novel Skeleton Conditioned Generative Adversarial Network (SCGAN) is designed to explore the correlation between skeleton‐level and pixel‐level motion prediction. Benefiting from SCGAN, the prediction of human regions is contributed by both coarse‐grained and fine‐grained motion features. This three‐level prediction, namely Progressive Prediction Video Anomaly Detection (P3VAD), enlarges the prediction error on irregular motion patterns. Besides, a pixel‐level analysis method is proposed to achieve Background‐bias Elimination (BE) and denoise the predicted region. Experimental results validate the effectiveness of P3VAD on the four benchmark datasets (ShanghaiTech, CUHK Avenue, IITB‐Corridor, and ADOC).

A high speed inference architecture for multimodal emotion recognition based on sparse cross modal encoder

In recent years, multimodal emotion recognition models are using pre-trained networks and attention mechanisms to pursue higher accuracy, which increases the training burden and slows down the training and inference speed. In order to strike a balance between speed and accuracy, this paper proposes a speed-optimized multimodal emotion recognition architecture for speech and text emotion recognition. In the feature extraction part, a lightweight residual graph convolutional network (ResGCN) is selected as the speech feature extractor, and an efficient RoBERTa pre-trained network is used as the text feature extractor. Then, an algorithm complexity-optimized sparse cross-modal encoder (SCME) is proposed and used to fuse these two types of features. Finally, a new gated fusion module (GF) is used to weight multiple results and input them into a fully connected layer (FC) for classification. The proposed method is tested on the IEMOCAP dataset and the MELD dataset, achieving weighted accuracies (WA) of 82.4% and 65.0%, respectively. This method achieves higher accuracy than the listed methods while having an acceptable training and inference speed.

A general ability for judging simple and complex ensembles.

People can report summary statistics for various features about a group of objects. One theory is that different abilities support ensemble judgments about low-level features like color versus high-level features like identity. Existing research mostly evaluates such claims based on evidence of correlations within and between feature domains. However, correlations between two identical tasks that only differ in the type of feature that is used can be inflated by method variance. Another concern is that conclusions about high-level features are mostly based on faces. We used latent variable methods on data from 237 participants to investigate the abilities supporting low-level and high-level feature ensemble judgments. Ensemble judgment was measured with six distinct tests, each requiring judgments for a distinct low-level or high-level feature, using different task requirements. We also controlled for other general visual abilities when examining how low-level and high-level ensemble abilities relate to each other. Confirmatory factor analyses showed a perfect correlation between the two factors, suggesting a single ability. There was a unique relationship between these two factors beyond the influence of object recognition and perceptual speed. Additional results from 117 of the same participants also ruled out the role of working memory. This study provides strong evidence of a general ensemble judgment ability across a wide range of features at the latent level and characterizes its relationship to other visual abilities. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Review of predicting protein stability changes upon variations.

Forecasting alterations in protein stability caused by variations holds immense importance. Improving the thermal stability of proteins is important for biomedical and industrial applications. This review discusses the latest methods for predicting the effects of mutations on protein stability, databases containing protein mutations and thermodynamic parameters, and experimental techniques for efficiently assessing protein stability in high-throughput settings. Various publicly available databases for protein stability prediction are introduced. Furthermore, state-of-the-art computational approaches for anticipating protein stability changes due to variants are reviewed. Each method's types of features, base algorithm, and prediction results are also detailed. Additionally, some experimental approaches for verifying the prediction results of computational methods are introduced. Finally, the review summarizes the progress and challenges of protein stability prediction and discusses potential models for future research directions.

A stream come true: Connecting tidal tails, shells, streams, and planes with galaxy kinematics and formation history

Context. The rapidly improving quality and resolution of both low surface brightness observations and cosmological simulations of galaxies enable us to address the important question of how the formation history is imprinted in the outer unrelaxed regions of galaxies, and to inspect the correlations of these imprints with another tracer of galaxy formation, the internal kinematics. Aims. Using the hydrodynamical cosmological simulation called Magneticum Pathfinder, we identified tidal tails, shells, streams, and satellite planes, and connected them to the amount of rotational support and the formation histories of the host galaxies. This presents the first combined statistical census considering all these four types of features in hydrodynamical cosmological simulations. Methods. Tidal features were visually classified from a three-dimensional rendering of the simulated galaxies by several scientists independently. Only features that were identified by at least half of the participating individuals were considered to be existing features. The data on satellite planes and kinematic properties of the simulated galaxies were taken from previous work. The results were compared to observations, especially from the MATLAS survey. Results. Generally, prominent features are much more common around elliptical than around disk galaxies. Shells are preferentially found around kinematically slowly rotating galaxies in both simulations and observations, while streams can be found around all types of galaxies, with a slightly higher probability to be present around less rotationally supported galaxies. Tails and satellite planes, however, appear independently of the internal kinematics of the central galaxy, indicating that they are formed through processes that have not (yet) affected the internal kinematics. Prolate rotators have the overall highest probability to exhibit tidal features, but the highest likelihood for a specific type of feature is found for galaxies with kinematically distinct cores (KDCs), nearly 20% of which exhibit streams. Conclusions. As shells are formed through radial merger events while streams are remnants of circular merger infall, this suggests that the orbital angular momentum of the merger event plays a more crucial role in transforming the host galaxy than previously anticipated. The existence of a shell around a given slow rotator furthermore is a sign of a radial merger formation for this particular slow rotator because one-third of the galaxies with a shell were transformed into slow rotators by the merger event that also caused the shells. The appearance of a stream around a KDC is a direct indicator for the multiple merger formation pathway of that KDC as opposed to the major merger pathway.

A review of vortex pinning in REBa2Cu3O7‐x coated conductors

AbstractREBa2Cu3O7‐x (REBCO) exhibits outstanding qualities such as a high irreversible field (7 T) and strong current‐carrying capacity (77 K, 106 A cm−2), making it ideal for high magnetic field applications such as fusion, accelerators, and nuclear magnetic resonance. However, the degradation of in‐field critical current density (Jc) due to vortex motion is a significant hurdle. Research indicates that improving vortex pinning can notably enhance Jc in REBCO, showing promise for its future high‐field applications. This review explores vortex pinning mechanisms, artificial pinning centers (APCs), and how environmental conditions affect pinning characteristics, highlighting advancements in vortex pinning for REBCO in magnetic fields. Different methods for introducing APCs are discussed and compared for evaluating their efficacy in achieving optimal nanoparticle distributions and consequent superconducting performance improvements, underscoring the importance of nano‐defect features (type, size, and distribution) in optimizing the superconducting properties of the REBCO. Additionally, the review tackles current challenges, shares recent research breakthroughs, and outlines future research directions for designing pinning landscapes to further improve REBCO performance.