Local Processing Research Articles

LSTM-DL: A Counting Optimization Approach for LSTM-Based DV-Hop Localization Model in Wireless Sensor Networks

The quest for precise localization of unknown nodes in Wireless Sensor Networks (WSN) garners significant academic attention, further intensified by the burgeoning domain of meta-heuristic algorithms. Traditional localization methods, such as the Least Squares Method (LSM), inherently yield lower accuracy, a limitation that this paper aims to transcend. We introduce a robust deep learning model grounded in Long Short-Term Memory (LSTM-DL) designed to enhance localization processes by effectively minimizing distance measurement errors in network topologies. This approach is augmented with an improved evaluation function, meticulously tailored to mitigate topological inaccuracies. Through rigorous experimental validation, the LSTM-DL model demonstrates a remarkable performance leap, surpassing the conventional DV-Hop method and outperforming existing meta-heuristic algorithms in accuracy and reliability. The findings decisively suggest that meta-heuristic approaches, and specifically the advanced LSTM-DL, hold substantial promise over traditional DV-Hop in the sphere of node localization within WSNs.

A method for building extraction in remote sensing images based on swintransformer

ABSTRACT Remote sensing image building segmentation, which is essential in land use and urban planning, is evolving with advancements in deep learning. Conventional methods using convolutional neural networks face limitations in integrating local and global information and establishing long-range dependencies, resulting in suboptimal segmentation in complex scenarios. This paper proposes LMSwin_PNet, a novel segmentation network that addresses the SwinTransformer encoder's deficiency in local information processing through a local feature extraction module. Additionally, it features a multiscale nonparametric merging attention module to enhance feature-channel correlations. The network also incorporates the pyramid large-kernel convolution module, replacing the traditional 3 × 3 convolution in the decoder with multibranch large-kernel convolution, thereby achieving a large receptive field and detailed information capture. Comparative analyses on three public building datasets demonstrated the model's superior segmentation performance and robustness. The results show that LMSwin_PNet produced outputs closely matching labels, showing its potential for broader application in remote sensing image segmentation tasks. It achieved achieving an IoU of 72.35% on the Massachusetts Building Dataset, 91.30% on the WHU Building Dataset, and 78.99% on the Inria aerial-image building dataset. The source code will be freely available at https://github.com/ziyanpeng/pzy.

Evolution of Hybrid Cellular Automata for Density Classification Problem

This paper describes a solution for the image density classification problem (DCP) using an entirely distributed system with only local processing of information named cellular automata (CA). The proposed solution uses two cellular automata’s features, density conserving and translation of the information stored in the cellular automata’s cells through the lattice, in order to obtain the solution for the density classification problem. The motivation for choosing a bio-inspired technique based on CA for solving the DCP is to investigate the principles of self-organizing decentralized computation and to assess the capabilities of CA to achieve such computation, which is applicable to many real-world decentralized problems that require a decision to be taken by majority voting, such as multi-agent holonic systems, collaborative robots, drones’ fleet, image analysis, traffic optimization, forming and then separating clusters with different values. The entire application is coded using the C# programming language, and the obtained results and comparisons between different cellular automata configurations are also discussed in this research.

The Role of Chemical Composition in Determining the Charge‐Carrier Dynamics in (AgI)x(BiI3)y Rudorffites

AbstractSilver‐bismuth‐based perovskite‐inspired materials (PIMs) are increasingly being explored as non‐toxic materials in photovoltaic applications. However, many of these materials exhibit an ultrafast localization of photogenerated charge carriers that is detrimental for charge‐carrier extraction. In this work, such localization processes are explored for thermally evaporated thin films of compositions lying along the (AgI)x(BiI3)y series, namely BiI3, AgBi2I7, AgBiI4, Ag2BiI5, Ag3BiI6, and AgI, to investigate the impact of changing Ag+/Bi3+ content. A persistent presence of ultrafast charge‐carrier localization in all mixed compositions and BiI3, together with unusually broad photoluminescence spectra, reveal that eliminating silver will not suppress the emergence of a localized state. A weak change in electronic bandgap and charge‐carrier mobility reveals the resilience of the electronic band structure upon modifications in the Ag+/Bi3+ composition of the mixed‐metal rudorffites. Instead, chemical composition impacts the charge‐carrier dynamics indirectly via structural alterations: Ag‐deficient compositions demonstrate stronger charge‐carrier localization most likely because a higher density of vacant sites in the cationic sublattice imparts enhanced lattice softness. Unraveling such delicate interplay between chemical composition, crystal structure, and charge‐carrier dynamics in (AgI)x(BiI3)y rudorffites provides crucial insights for developing a material‐by‐design approach in the quest for highly efficient Bi‐based PIMs.

Attention-Based Two-Branch Hybrid Fusion Network for Medical Image Segmentation

Accurate segmentation of medical images is vital for disease detection and treatment. Convolutional Neural Networks (CNN) and Transformer models are widely used in medical image segmentation due to their exceptional capabilities in image recognition and segmentation. However, CNNs often lack an understanding of the global context and may lose spatial details of the target, while Transformers struggle with local information processing, leading to reduced geometric detail of the target. To address these issues, this research presents a Global-Local Fusion network model (GLFUnet) based on the U-Net framework and attention mechanisms. The model employs a dual-branch network that utilizes ConvNeXt and Swin Transformer to simultaneously extract multi-level features from pathological images. It enhances ConvNeXt’s local feature extraction with spatial and global attention up-sampling modules, while improving Swin Transformer’s global context dependency with channel attention. The Attention Feature Fusion module and skip connections efficiently merge local detailed and global coarse features from CNN and Transformer branches at various scales. The fused features are then progressively restored to the original image resolution for pixel-level prediction. Comprehensive experiments on datasets of stomach and liver cancer demonstrate GLFUnet’s superior performance and adaptability in medical image segmentation, holding promise for clinical analysis and disease diagnosis.

Focus effect unveils children’s local processing of pronouns and reflexives

ABSTRACT Studies on young children’s comprehension have shown that children can experience problems interpreting object pronouns, even when reflexive interpretation is already adult-like. Compared to resolving reflexives, linking pronouns to a referent is considered a more “intensive” process, because it also involves non-syntactic factors like discourse context. This could explain why children experience more difficulties with pronouns than with reflexives. Using eye-tracking and a truth value judgement task, we investigated the effect of focus via it-clefts on the processing of reflexives and pronouns in German-speaking children and adults. We analyzed gaze data of two time segments: before and during the mention of the pronoun/reflexive. The cleft segment revealed similar processing of it-clefts in children and adults. In the subsequent reflexive/pronoun segment, clefts caused adults to pay overall more attention to the local referent, while children fixated the clefted non-local referent more. The difference in focus effect, that is, children attend the clefted referent more, while adults pay more attention to the non-clefted referent, helped uncover processing differences between children and adults. That is, unlike adults, children consider only the local discourse context during referential processing. We argue that these processing differences cause children’s interpretation difficulties. However, the offline data showed no effect of information structure, suggesting that whether the processing differences transfer to the final interpretation depends on the language-specific function of the pronoun system, which may aid in restricting referential links.

Optimizing Hadoop Scheduling in Single-Board-Computer-Based Heterogeneous Clusters

Single-board computers (SBCs) are emerging as an efficient and economical solution for fog and edge computing, providing localized big data processing with lower energy consumption. Newer and faster SBCs deliver improved performance while still maintaining a compact form factor and cost-effectiveness. In recent times, researchers have addressed scheduling issues in Hadoop-based SBC clusters. Despite their potential, traditional Hadoop configurations struggle to optimize performance in heterogeneous SBC clusters due to disparities in computing resources. Consequently, we propose modifications to the scheduling mechanism to address these challenges. In this paper, we leverage the use of node labels introduced in Hadoop 3+ and define a Frugality Index that categorizes and labels SBC nodes based on their physical capabilities, such as CPU, memory, disk space, etc. Next, an adaptive configuration policy modifies the native fair scheduling policy by dynamically adjusting resource allocation in response to workload and cluster conditions. Furthermore, the proposed frugal configuration policy considers prioritizing the reduced tasks based on the Frugality Index to maximize parallelism. To evaluate our proposal, we construct a 13-node SBC cluster and conduct empirical evaluation using the Hadoop CPU and IO intensive microbenchmarks. The results demonstrate significant performance improvements compared to native Hadoop FIFO and capacity schedulers, with execution times 56% and 22% faster than the best_cap and best_fifo scenarios. Our findings underscore the effectiveness of our approach in managing the heterogeneous nature of SBC clusters and optimizing performance across various hardware configurations.

Iris Recognition based on Statistically Bound Spatial Domain Zero Crossing and Neural Networks

Purpose The iris pattern is an important biological feature of the human body. The recognition of an individual based on an iris pattern is gaining more popularity due to the uniqueness of the pattern among the people. Iris recognition systems have received attention very much due to their rich iris texture which gives robust standards for identifying individuals. Notwithstanding this, there are several challenges in unrestricted recognition environments. Methods This article discusses a highly error-resistant technique to implement a biometric recognition system based on the iris portion of the human eye. All iris recognition algorithms of the current day face a major problem of localization errors and the enormous time involved in this localization process. Spatial domain zero crossing may be the simplest and least complex method for localization. Yet, it has not been used due to its high sensitivity to erroneous edges, as a consequence of which more complex and time-consuming algorithms have taken its place. Appropriate statistical bounds imposed on this process help this method to be the least erroneous and time-consuming. Errors were reduced to 0.022% using this approach on the CASIA v1 & v2 datasets. Time consumption in this stage was the least compared to other algorithms. At the comparison stage, most algorithms use multiple comparisons to account for translation and rotation errors. This is time-consuming and very resource-hungry. Results The current approach discusses a robust method based on a single comparison, which works with a correct recognition of over 99.78% which is clearly demonstrated by tests. Conclusions The technique is to use a neural network trained to recognize special statistical and regional parameters unique to every person’s iris. The algorithm also gives sufficient attention to consider illumination errors, elliptical pupils, excess eyelash errors and bad contrast.

Integrated Transcriptomic and Metabolomic Analysis Reveal the Dynamic Process of Bama Hemp Seed Development and the Accumulation Mechanism of α-Linolenic Acid and Linoleic Acid.

Bama County is a world-famous longevity county in the Guangxi Province, China. Bama hemp is a traditional seed used in hemp cultivation in the Bama County. The seeds contain abundant unsaturated fatty acids, particularly linoleic acid (LA) and linolenic acid in the golden ratio. These two substances have been proven to be related to human health and the prevention of various diseases. However, the seed development and seed oil accumulation mechanisms remain unclear. This study employed a combined analysis of physiological, transcriptomic, and metabolomic parameters to elucidate the fatty acid formation patterns in Bama hemp seeds throughout development. We found that seed oil accumulated at a late stage in embryo development, with seed oil accumulation following an "S″-shaped growth curve, and positively correlated with seed size, sugar content, protein content, and starch content. Transcriptome analysis identified genes related to the metabolism of LA, α-linolenic acid (ALA), and jasmonic acid (JA). We found that the FAD2 gene was upregulated 165.26 folds and the FAD3 gene was downregulated 6.15 folds at day 21. Metabolomic changes in LA, ALA, and JA compounds suggested a competitive relationship among these substances. Our findings indicate that the peak period of substance accumulation and nutrient accumulation in Bama hemp seeds occurs during the midstage of seed development (day 21) rather than in the late stage (day 40). The results of this research will provide a theoretical basis for local cultivation and deep processing of Bama hemp.

Comprehensive Performance Evaluation between Visual SLAM and LiDAR SLAM for Mobile Robots: Theories and Experiments

SLAM (Simultaneous Localization and Mapping), primarily relying on camera or LiDAR (Light Detection and Ranging) sensors, plays a crucial role in robotics for localization and environmental reconstruction. This paper assesses the performance of two leading methods, namely ORB-SLAM3 and SC-LeGO-LOAM, focusing on localization and mapping in both indoor and outdoor environments. The evaluation employs artificial and cost-effective datasets incorporating data from a 3D LiDAR and an RGB-D (color and depth) camera. A practical approach is introduced for calculating ground-truth trajectories and during benchmarking, reconstruction maps based on ground truth are established. To assess the performance, ATE and RPE are utilized to evaluate the accuracy of localization; standard deviation is employed to compare the stability during the localization process for different methods. While both algorithms exhibit satisfactory positioning accuracy, their performance is suboptimal in scenarios with inadequate textures. Furthermore, 3D reconstruction maps established by the two approaches are also provided for direct observation of their differences and the limitations encountered during map construction. Moreover, the research includes a comprehensive comparison of computational performance metrics, encompassing Central Processing Unit (CPU) utilization, memory usage, and an in-depth analysis. This evaluation revealed that Visual SLAM requires more CPU resources than LiDAR SLAM, primarily due to additional data storage requirements, emphasizing the impact of environmental factors on resource requirements. In conclusion, LiDAR SLAM is more suitable for the outdoors due to its comprehensive nature, while Visual SLAM excels indoors, compensating for sparse aspects in LiDAR SLAM. To facilitate further research, a technical guide was also provided for the researchers in related fields.

Determination of Clay and Sand Volume Using Vertical Electrical Sounding

This study presents the results of an electrical resistivity survey conducted in Agudama Epie, Yenagoa local government area, Bayelsa State, Nigeria. The primary objective of the survey was to estimate the volume of clay and sand within a rectangular land parcel measuring 100 m by 200m in the study area. The survey employed the Schlumberger electrode configuration and utilized nine vertical electrode sounding (VES) points.Data was collected using a Terrameter SAS 1000 and was processed using IPI2win software. The geophysical analysis revealed the presence of four distinct geoelectric layers across the study area, identified as layers 1 to 4 in the VES profiles. Layer 2, characterized by clay, exhibited an average thickness of 21.2 meters, while layer 3, consisting of sand, had an average thickness of 8.15 meters. The apparent resistivity values ranged from 8.45 to 79.43 ohm-meter to a depth of 10.14 meters for the clay layer and 8499.86 to 15013.11 ohm-meter to a depth of 33.5 meters for the sand layer. The study estimated a substantial presence of approximately 746,240 tonnes of clay and approximately 261,126 tonnes of sand in Agudama Epie. This discovery offers significant economic potential, including job creation, the establishment of small and medium-sized enterprises, and opportunities within the construction industry. The local extraction and processing of clay can drive economic growth and facilitate the production of concrete and building materials. Additionally, the availability of sand in the region can reduce transportation costs and stimulate local construction projects, thereby fostering economic activity and infrastructure development.

47.5‐inch 8K inkjet printing AMOLED monitor with local boosting gate driver on array design

AbstractIn this paper, we presented a 47.5 in. 8K2K AMOLED MNT employing a low temperature poly‐silicon (LTPS) backplane, top emissive inkjet printing (IJP) OLED devices, and gate driver on array (GOA) supporting independent 32 * 9 zone control and internal compensation driving. By combining printed RGB units with a common evaporative blue stack, we proposed the hybrid tandem IJP devices that improved both device efficiency and operation load balance. In the local boosting GOA design, a novel architecture for independent display partition control was developed. The GOA cascading and timing scheme was carefully considered by reusing several clock signals to support both TFT internal compensation and display zone control. It is so far the first prototype AMOLED MNT integrating inkjet printing process and local high frame rate (up to 960 Hz) driving. Thanks to the 8K2K high resolution, local frame rate data processing, and IJP OLED EL reaching HDR brightness up to 580 nits with superior optical performance, it shows a brand‐new approach to deliver a premium display.

Cloud-magnetic resonance imaging system: In the era of 6G and artificial intelligence

Magnetic resonance imaging (MRI) plays an important role in medical diagnosis, generating petabytes of image data annually in large hospitals. This voluminous data stream requires a significant amount of network bandwidth and extensive storage infrastructure. Additionally, local data processing demands substantial manpower and hardware investments. Data isolation across different healthcare institutions hinders cross-institutional collaboration in clinics and research. In this work, we anticipate an innovative MRI system and its four generations that integrate emerging distributed cloud computing, 6G bandwidth, edge computing, federated learning, and blockchain technology. This system is called Cloud-MRI, aiming at solving the problems of MRI data storage security, transmission speed, artificial intelligence (AI) algorithm maintenance, hardware upgrading, and collaborative work. The workflow commences with the transformation of k-space raw data into the standardized Imaging Society for Magnetic Resonance in Medicine Raw Data (ISMRMRD) format. Then, the data are uploaded to the cloud or edge nodes for fast image reconstruction, neural network training, and automatic analysis. Then, the outcomes are seamlessly transmitted to clinics or research institutes for diagnosis and other services. The Cloud-MRI system will save the raw imaging data, reduce the risk of data loss, facilitate inter-institutional medical collaboration, and finally improve diagnostic accuracy and work efficiency.

Theoretical and experimental study of strain localization phenomenon based on phase transition theory

In the study of large-scale localized strain features, localized strain can lead to energy release and seismic effects. From the macroscopic process of localized strain, it is found that localized deformation is accompanied by structural weakening, which is due to phase changes in rocks. The phase change corresponds to the secondary phase transition process in physics. The present study illustrates this phenomenon from the perspective of secondary phase transition theory in statistical physics, combining the localized strain features on a large scale. Theoretical analysis and experimental studies were carried out using three brittle rocks, including marble, granite and red sandstone. First, two perturbation methods, i.e., Krylov–Bogoliubov method and Poincare method, are used to calculate the higher-order control equations in analytical model. The influences of control equation coefficients on the strain localization process are analyzed. Then the uniaxial compression tests are carried out on the three rocks to record the strain process. Finally, the theoretical and experimental results are compared to analyze the strain localization phenomenon. The comparison results show that both the Krylov–Bogoliubov solution and the Poincare solution in the analytical model can well describe the evolution characteristics of localized strain. It indicates that the theoretical model is valid and has high accuracy. The Poincare method better simulates the nonlinear phenomenon of strain localization. The present study provides a new theoretical method to better understand the strain localization phenomenon.

Associations Between Neighborhood Resources and Youths’ Response to Reward Omission in a Task Modeling Negatively Biased Environments

Neighborhoods provide essential resources (eg, education, safe housing, green space) that influence neurodevelopment and mental health. However, we need a clearer understanding of the mechanisms mediating these relationships. Limited access to neighborhood resources may hinder youths from achieving their goals and, over time, shape their behavioral and neurobiological response to negatively biased environments blocking goals and rewards. To test this hypothesis, 211 youths (aged ∼13.0 years, 48% boys, 62% identifying as White, 75% with a psychiatric disorder diagnosis) performed a task during functional magnetic resonance imaging. Initially, rewards depended on performance (unbiased condition); but later, rewards were randomly withheld under the pretense that youths did not perform adequately (negatively biased condition), a manipulation that elicits frustration, sadness, and a broad response in neural networks. We investigated associations between the Childhood Opportunity Index (COI), which quantifies access to youth-relevant neighborhood features in 1 metric, and the multimodal response to the negatively biased condition, controlling for age, sex, medication, and psychopathology. Youths from less-resourced neighborhoods responded with less anger (p< .001, marginal R2= 0.42) and more sadness (p< .001, marginal R2= 0.46) to the negatively biased condition than youths from well-resourced neighborhoods. On the neurobiological level, lower COI scores were associated with a more localized processing mode (p= .039, marginal R2= 0.076), reduced connectivity between the somatic-motor-salience and the control network (p= .041, marginal R2= 0.040), and fewer provincial hubs in the somatic-motor-salience, control, and default mode networks (all pFWE< .05). The present study adds to a growing literature documenting how inequity may affect the brain and emotions in youths. Future work should test whether findings generalize to more diverse samples and should explore effects on neurodevelopmental trajectories and emerging mood disorders during adolescence. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented sexual and/or gender groups in science. One or more of the authors of this paper received support from a program designed to increase minority representation in science. We actively worked to promote sex and gender balance in our author group. We actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our author group.

Tongue focal myositis: Case series and immunohistochemical characterization

AbstractIntroductionFocal myositis (FM) is an uncommon, localized inflammatory process that affects a single muscle, with unknown aetiology. In the head and neck region, involvement of perioral musculature, floor of the mouth, and sternocleidomastoid muscle have been reported. FM affecting the tongue is very rare, with only four cases previously published to date.AimThis report describes five additional cases of tongue FM.Case ReportThree patients were male and two were female (mean age, 56 years), and three had symptomatic short‐term nodular swelling.ResultsBy immunohistochemistry, M2 macrophages and CD4+ T cells were predominant. Focally, CD15+ granulocytes and CD20+ B cells were visualized. Polyclonal CD138+ plasma cells, with a predominance of IgG+ cells (rarely IgG4+), were detected. All patients evolved with complete resolution after biopsy and no signs of clinical or systemic progression were observed during the follow‐up.ConclusionsFM should be considered in the differential diagnosis of rapidly growing nodular lingual lesions. By immunohistochemistry, a predominance of M2 macrophages and CD4+ T cells is typical, and it can help in the differential diagnosis with myositis of other origins.

4f electron temperature driven ultrafast electron localization

Valence transitions in strongly correlated electron systems are caused by orbital hybridization and Coulomb interactions between localized and delocalized electrons. The transition can be triggered by changes in the electronic structure and is sensitive to temperature variations, applications of magnetic fields, and physical or chemical pressure. Launching the transition by photoelectric fields can directly excite the electronic states and thus provides an ideal platform to study the correlation among electrons on ultrafast timescales. The EuNi2(Si0.21Ge0.79)2 mixed-valence metal is an ideal material to investigate the valence transition of the Eu ions via the amplified orbital hybridization by the photoelectric field on subpicosecond timescales. A direct view on the 4f electron occupancy of the Eu ions is required to understand the microscopic origin of the transition. Here we probe the 4f electron states of EuNi2(Si0.21Ge0.79)2 at the sub-ps timescale after photoexcitation by x-ray absorption spectroscopy across the Eu M5-absorption edge. The observed spectral changes due to the excitation indicate a population change of total angular momentum multiplet states J = 0, 1, 2, and 3 of Eu3+ and the Eu2+ J = 7/2 multiplet state caused by an increase in 4f electron temperature that results in a 4f localization process. This electronic temperature increases combined with fluence-dependent screening accounts for the strongly nonlinear effective valence change. The data allow us to extract a time-dependent determination of an effective temperature of the 4f shell, which is also of great relevance in the understanding of metallic systems' properties, such as the ultrafast demagnetization of ferromagnetic rare-earth intermetallic and their all-optical magnetization switching. In addition, our results elucidate the energetics of charge fluctuations in valence-mixed electronic systems, which provide enriched knowledge regarding the role of valence transitions and orbital hybridization for quantum critical phenomena. Published by the American Physical Society 2024

In the Name of Mazu: The Use of Religion by China to Intervene in Taiwanese Elections

Abstract Foreign policy objectives can be achieved through religious institutions as a form of soft power. However, empirical evidence on this form of soft power is limited. In this study, we provide rigorous empirical evidence of electoral influence through the channel of religious institutions. To test the efficacy of this channel of influence, we study the Chinese government’s attempts to influence Taiwanese elections through religious institutions. By leveraging spatial regression methods, we demonstrate that Mazu temples influence electoral behavior through a localized process. The density of urban Mazu temples in Kaohsiung, Taiwan, which are subject to influence from China, is associated with an increase in vote share for the pro-China party in their neighborhoods. However, the influence on voters in urban and village areas is not homogeneous. In contrast to urban temples, the relationship between village Mazu temples and the electoral results is insignificant.

Research on the Application of Recognition and Detection Technology in Automatic Driving

Object detection is the cornerstone of autonomous driving systems. In autonomous driving systems, vehicle detection is critical to maintaining traffic flow and avoiding collisions. The use of deep learning technology enables the system to skillfully and accurately identify vehicles on the road. Deep learning algorithms can provide autonomous vehicles with precise environmental awareness to enable informed driving decisions. Deep learning algorithms can achieve pedestrian detection. Such algorithms can improve the safety of vehicle operations. Through deep learning models, the system is able to identify and track pedestrians, ensuring appropriate responses in different traffic scenarios. The application of detection technology enables vehicles to promptly identify and interpret road signs and traffic lights in real-time. This recognition ability helps to comprehensively understand traffic conditions and improve the ability to adapt to different road conditions. This study provides an in-depth exploration of the importance and application of deep learning technology in the field of autonomous driving. Special attention is paid to the key role played by object detection methods. and propose future development directions. Deep learning algorithms can expand the application of learning technology to handle real-time traffic conditions and achieve rapid response. Achieve the fusion of multiple sensory information by improving the local processing capabilities of edge computing. This research aims to further improve the level of intelligence inherent in autonomous driving systems.

Shear localization-induced amorphization in nanocrystals during high strain rate deformation

Shear localization is the predominant form of failure of metals at high strain rates where amorphization often occurs. However, the mechanism and role of amorphization in shear localization are not yet clearly understood. In this work, a series of shear deformations of nanocrystals with different grain sizes at high strain rates are investigated using molecular dynamics simulations. The results show that amorphization is not the result of thermal melting, but a new plastic deformation mechanism in the process of shear localization, which serves as a complement to dislocation and grain boundary to support plastic deformation at a high strain rate. The critical shear strain at which shear localization and amorphization occur is greater for small-grained specimens than for large-grained specimens. This can be attributed to the fact that small-grained specimens have more defects and slip systems that can be activated, which cause amorphization to occur later. The strain rate has a significant influence on shear localization and amorphization. The processes of shear localization and amorphization become more complicated at extremely high strain rates than at relatively low strain rates.