Network Structure Research Articles

Multistream Adaptive Attention-Enhanced Graph Convolutional Networks for Youth Fencing Footwork Training.

The popularity of fencing and intense sports competition has burdened adolescents with excessive training, harming their immature bodies. Traditional training methods fail to provide timely movement corrections and personalized plans, leading to ineffective exercises. This paper aims to use artificial intelligence technology to reduce ineffective exercises and alleviate the training burden. We propose an action recognition algorithm based on the characteristics of adolescent athletes. This algorithm uses multimodal input data to comprehensively extract action information. Each modality is processed by the same network structure, utilizing attention mechanisms and adaptive graph structures. A multibranch feature fusion method is used to determine the final action category. We gathered the fencing footwork data set 2.0. Our model achieved 93.3% accuracy, with the highest precision at 95.8% and the highest F1-Score at 94.5% across all categories. It effectively recognized actions of adolescents with different heights and speeds, outperforming traditional methods. Our artificial intelligence-based training solution improves training efficiency and reduces the training burden on adolescents.

Network community detection via neural embeddings

Recent advances in machine learning research have produced powerful neural graph embedding methods, which learn useful, low-dimensional vector representations of network data. These neural methods for graph embedding excel in graph machine learning tasks and are now widely adopted. However, how and why these methods work—particularly how network structure gets encoded in the embedding—remain largely unexplained. Here, we show that node2vec—shallow, linear neural network—encodes communities into separable clusters better than random partitioning down to the information-theoretic detectability limit for the stochastic block models. We show that this is due to the equivalence between the embedding learned by node2vec and the spectral embedding via the eigenvectors of the symmetric normalized Laplacian matrix. Numerical simulations demonstrate that node2vec is capable of learning communities on sparse graphs generated by the stochastic blockmodel, as well as on sparse degree-heterogeneous networks. Our results highlight the features of graph neural networks that enable them to separate communities in the embedding space.

Advancing complex urban traffic forecasting: A fully attentional spatial-temporal network enhanced by graph representation

Accurate urban traffic forecasting is essential for intelligent transportation systems (ITS). However, the majority of existing forecasting methodologies predominantly concentrate on point-based forecasts (e.g., traffic detector forecasts). A limited number of them pay attention to the urban bidirectional road segments and the complex road network topology. To advance accurate traffic forecasting in complex urban scenarios, this paper proposes a Graph Representation enhanced Fully Attentional Spatial-Temporal network (GR-FAST). First, we construct a refined bidirectional road network graph (BRG) to depict the urban road network topology more accurately, particularly focusing on the turning patterns at intersections. Then, we adopt the graph representation methodology and introduce spatial information encoding (SIE) to explicitly characterize the significance of roads and network structure from multiple perspectives. Enhanced by SIE, spatial attention can capture spatial dependencies from both road network topologies and traffic pattern similarities, thereby forming a unified urban spatial cognition. Finally, a multi-scale residual perception (MRP) module is designed to balance the interplay of short-term temporal variability and long-term periodicity. Experiments on a real-world urban dataset from Wuhan, China, demonstrate that GR-FAST outperforms the state-of-the-art deep learning methods, achieving an improvement of 9.19%. Furthermore, ablation studies suggest that the explicit incorporation of complex road spatial topologies can significantly enhance forecasting accuracy.

Source localization in signed networks based on dynamic message passing algorithm

Sign characteristics of social networks play a crucial role in the process of information dissemination, and structural balance theory is a significant property of signed networks. Despite some progress in source localization, the sign features of network structures are widely overlooked. Furthermore, the impact of structural balance on tracing remains unclear. In this paper, we explore a model of information propagation in signed networks and investigate source localization in the context of a given network state snapshot. We first propose a modified signed susceptibility-infection-recovery (S-SIR) propagation model, combining the positive/negative transmission rates based on structural balance theory, which is designed to better approximate the real-world social network structure. Next, a signed dynamic message passing algorithm (S-DMP) is proposed for source localization. Simulation experiments show that the S-DMP algorithm can fully utilize both the positive and negative attributes of the edges in the signed network and outperforms other methods in terms of source localization accuracy. These experiments prove that the proposed method can be used for source localization in the early stage of information transmission. Furthermore, we find that the accuracy of source localization is highest when there is a balance between the number of positive and negative edges.

HF Trimer: A New Full-Dimensional Potential Energy Surface and Rigorous 12D Quantum Calculations of Vibrational States.

HF trimer, as the smallest and the lightest cyclic hydrogen-bonded (HB) cluster, has long been a favorite prototype system for spectroscopic and theoretical investigations of the structure, energetics, spectroscopy, and dynamics of hydrogen-bond networks. Recently, rigorous quantum 12D calculations of the coupled intra- and intermolecular vibrations of this fundamental HB trimer (J. Chem. Phys. 2023, 158, 234109) were performed, employing an older ab initio-based many-body potential energy surface (PES). While the theoretical results were found to be in reasonably good agreement with the available spectroscopic data, it was also evident that it is highly desirable to develop a more accurate 12D PES of HF trimer. Motivated by this, here we report a new, and the first fully ab initio 12D PES of this paradigmatic system. Approximately 42,540 geometries were sampled and calculated at the level of CCSD(T)-F12a/AVTZ. The permutationally invariant polynomial-neural network based Δ-machine learning approach (J. Phys. Chem. Lett. 2022, 13, 4729) was employed to perform cost-efficient calculations of the basis-set-superposition error (BSSE) correction. By strategically selecting data points, this approach facilitated the construction of a high-precision PES with BSSE correction, while requiring only a minimal number of BSSE value computations. The fitting error of the final PES is only 0.035 kcal/mol. To assess its performance, the 12D fully coupled quantum calculations of excited intra- and intermolecular vibrational states of HF trimer are carried out using the rigorous methodology developed by us earlier. The results are found to be in a significantly better agreement with the available spectroscopic data than those obtained with the previously existing semiempirical 12D PES.

Relationship Between Aquatic Factors and Sulfide and Ferrous Iron in Black Bloom in Lakes: A Case Study of a Eutrophic Lake in Eastern China

Black bloom is a very serious water pollution phenomenon in eutrophic lakes, with Fe(II) and S(−II) being the key limiting factors for this problem. In this paper, three different machine learning methods, namely, Random Forest (RF), Gaussian Mixture Model (GMM), and Bayesian Network (BN), were used to explore the complex interactions among Fe(II), S(−II), and other aquatic factors in the estuary of Chaohu Lake to better characterize and monitor water degradation by black bloom. The results of RF showed that total nitrogen (TN), ammonia, total phosphorous (TP), suspended sediment concentration (SSC), and oxidation–reduction potential (ORP), which were chosen from 11 factors, had the most important relationships with Fe(II) and S(−II). The 69 sampling sites were divided in three groups identified as worst, worse, and bad according to the observed values of seven factors using the GMM. Then, the BN model was applied to three observation groups. The results showed that the structures of the interaction networks were different between the groups. S(−II) controlled only SSC production in the bad and worse group sites, while SSC was determined by both S(−II) and Fe(II) in the worst group. Ammonia and TN exhibited the most direct importance for S(−II) and Fe(II) production in all observation groups. According to the indications from the BNs, potential management strategies for different water pollution conditions were developed. Finally, the threshold values of Fe(II), S(−II), TP, ammonia, TN, SSC, and ORP, which were 0.80 mg/L, 0.04 mg/L, 0.45 mg/L, 3.44 mg/L, 4.15 mg/L, 55 mg/L, and 135 mv, respectively, were determined on the basis of the BN models. These values will be helpful to develop accurate strategies of oxygenation to quickly eliminate black bloom in the lake.

Serving Innovation: Exploring Patent Agency Service Networks in China

ABSTRACTPatent agency services are crucial for the codification of technical knowledge and inventions. Drawing on an inter‐firm network analysis of the patent agency service relationships between patent agencies and their innovation clients during the last two decades in China, this paper seeks to explore the structure of urban patent agency service networks and how it is shaped by China's unique institutional and market forces. The study found that although the patent agency service network generally follows the basic structure of China's innovative and economic landscapes, it is still deeply shaped by China's unique intellectual property institutions, administrative hierarchy factors, market forces driven by patent agency service demand, and multidimensional proximities in a complex manner. Analyzing how these factors function should be related to the characteristics of patent agencies and the actors of networks. The study will help us understand how city patent service networks might be shaped by institutional and spatial factors, and provide new evidence on how cities are interlinked within patent agency service networks.

Scalable parallel photonic processing unit for various neural network accelerations

In recent years, integrated optical processing units (IOPUs) have demonstrated advantages in energy efficiency and computational speed for neural network inference applications. However, limited by optical integration technology, the practicality and versatility of IOPU face serious challenges. In this work, a scalable parallel photonic processing unit (SPPU) for various neural network accelerations based on high-speed phase modulation is proposed and implemented on a silicon-on-insulator platform, which supports parallel processing and can switch between multiple computational paradigms simply and without latency to infer different neural network structures, enabling to maximize the utility of on-chip components. The SPPU adopts a scalable and process-friendly architecture design, with a preeminent photonic-core energy efficiency of 0.83 TOPS/W, two to ten times higher than existing integrated solutions. In the proof-of-concept experiment, a convolutional neural network (CNN), a residual CNN, and a recurrent neural network (RNN) are all implemented on our photonic processor to handle multiple tasks of handwritten digit classification, signal modulation format recognition, and review emotion recognition. The SPPU achieves multi-task parallel processing capability, serving as a promising and attractive research route to maximize the utility of on-chip components under the constraints of integrated technology, which helps to make IOPU more practical and universal.

Suicidality Is Most Centrally Situated Within Network of Depression Symptom Criteria in Unipolar Depression Patients With Mood Stabilizer in Asia

Lithium and mood stabilizers are considered effective augmentation agents of antidepressants for treatment-resistant depression. Thus, this study aimed to estimate the network structure of depression symptom criteria among unipolar depression patients with mood stabilizers, using data from the Research on Asian Psychotropic Prescription Patterns for mood stabilizers (REAP-MS). We estimated a network of the 9 depression symptom criteria among 411 unipolar depression patients in Asia. Each of the depression symptom criteria was considered to be a dichotomous categorical variable. Suicidality (suicidal ideation or attempt) was the most centrally situated within the network of depression symptoms, followed by depressed mood, loss of energy, anhedonia and weight loss or gain. Contrastingly, concentration problem was the least interconnected. The depression symptom criteria were organized into 4 clusters by the community detection method. The findings suggest that suicidality may be one of the significant therapeutic target symptoms in unipolar depression patients with mood stabilizers.

Split tensile strength of fiber-reinforced coral aggregate concrete: Deep learning model and experimental validation

Machine learning methods are increasingly becoming an important research field for solving the complex mechanical behavior of concrete materials. A reliable deep learning model was developed based on a dataset consisting of 319 experimental data points to predict the split tensile strength of fiber-reinforced coral aggregate concrete (FRCAC). The feature selection was carried out using correlation analysis, and the neural network structure was optimized by adjusting hyperparameters based on the 10-fold cross-validation (CV). Subsequently, six statistical indicators were employed to evaluate the accuracy and generalization ability of the model. The deep neural network (DNN) model demonstrates excellent performance, with R2, MAE, and MAPE of 0.98, 0.181 MPa, and 0.037 for the training dataset and 0.95, 0.286 MPa, and 0.074 for the test dataset, respectively. Additionally, 24 sets of concrete proportions are designed using the trained DNN model, and the results indicate that the absolute error and relative error between predicted and experimental values are less than 0.4 MPa and 6.8 %, respectively. The feature analysis based on SHapley Additive exPlanations (SHAP) reveals that volume fraction, curing age, water-binder ratio, and the tensile strength of fiber have the highest contribution in estimating the split tensile strength of FRCAC. This research can provide a reference for optimizing the design of FRCAC.

The Many Tails That Critique Bites: Malabou's Anarchist Turn and the Metaphysics of Biology

This article explores the relation between politics and biology in Catherine Malabou's work, traces the origin of her recent anarchist turn, and seeks to explain how the latter influences the concept of plasticity. Whereas the relation between plasticity, neuroscience and epigenetics reflected a certain affinity with Marxism in her earlier work, Malabou's recent claim that biology and ontology are anarchist remains opaque as to its grounding in her own thought and scientific developments alike. The article argues that the origin of the metamorphosis of plasticity from a Marxist to an anarchist notion gives rise to a new metaphysics of biology that is based on the assumption that society needs to be brain-like, reflecting the networked structure of synaptic plasticity. The article also proposes a new reading of Malabou's three plasticities (negative, positive and the plasticity of the concept) in temporal terms.

Application of bacterial-derived long cellulose nanofiber to suspension culture of mammalian cells as a shear protectant

Nanofibrillated bacterial cellulose (NFBC) is a bio-compatible long-fiber nanocellulose produced by cellulose-synthesizing bacteria. It forms an entangled network structure in the suspension state, thereby imparting greater viscosity than conventional media additives. In this study, we examined its application as a shear protectant in the suspension culture of mammalian cells to mitigate hydrodynamic stress imposed on the cells. The media supplemented with hydroxypropyl cellulose-adsorbed NFBC (HP-NFBC) exhibited an increase in shear viscosity according to rheometric analysis, similar to FP003, a commercially available medium additive. Suspension culture of Chinese hamster ovary cells in HP-NFBC-containing media under a high stirring rate (120 rpm) demonstrated higher cell growth and lower cell death compared to those in the medium without additives and in FP003. A 0.10 (w/v)% concentration of HP-NFBC showed the highest viable cell number among the tested concentrations. Computational fluid dynamics simulation revealed a decrease in shear rate and flow velocity within the spinner flask owing to the addition of HP-NFBC or FP003. It is suggested that the decline of these parameters in high-viscosity media suppresses the hydrodynamic stress on cells. This study highlights the potential of HP-NFBC as a shear protectant in mammalian cell suspension culture.

Leather-Based Shoe Soles for Real-Time Gait Recognition and Automatic Remote Assistance Using Machine Learning.

Real-time monitoring of gait characteristics is crucial for applications in health monitoring, patient rehabilitation feedback, and telemedicine. However, the effective and stable acquisition and automatic analysis of gait information remain significant challenges. In this study, we present a flexible sensor based on a carbon nanotube/graphene composite conductive leather (CGL), which uses collagen fiber with a three-dimensional network structure as the flexible substrate. The CGL-based sensor demonstrates a high dynamic range, with notable pressure responses ranging from 0.6 to 14.5 kPa and high sensitivity (S = 0.2465 kPa-1). We further developed a device incorporating the CGL-based sensor to collect foot characteristic signals from human motion and designed smart sports shoes to facilitate effective human-computer interaction. Machine learning was employed to collect and process gait characteristic information in various states, including standing, sitting, walking, and falling. For real-time monitoring of falls, we optimized the K-Nearest Time Series Classifier (KNTC) algorithm, achieving an accuracy of 0.99 and a prediction time of only 13 ms, which highlights the system's excellent intelligent response capabilities. The system maintained a gait recognition accuracy of 90% across diverse populations, with low false-positive (3.3%) and false-negative (3.3%) rates. This work demonstrates stable gait recognition capabilities and provides valuable methods and insights for plantar behavior monitoring and data analysis, contributing to the development of advanced real-time gait monitoring systems.

Structural changes of potato starch and activity inhibition of starch digestive enzymes by anthocyanin from lingonberry (Vaccinium uliginosum L.) retarded starch digestibility

The effects of anthocyanins on in vitro and in vivo digestibility of potato starch were evaluated in this study. Then the influence of anthocyanins on physicochemical property of potato starch and the activity of starch digestive enzymes (α-amylase and α-glucosidase) were also investigated to understand the mechanism of anthocyanins on starch digestibility. Results have shown that dietary anthocyanins could effectively inhibit the biological activities of α-amylase and α-glucosidase to delay the peak of postprandial blood glucose. Characterization of physicochemical properties of potato starch indicates a structural change due to the presence of anthocyanins, hindering its access to starch digestive enzymes. Among all anthocyanins, lingonberry anthocyanin significantly promoted the retrogradation of potato starch (7.153 % to 25.913 %) and exert promising inhibition on α-amylase and α-glucosidase. Lingonberry anthocyanins mainly interacted with potato starch through hydrogen bonds, which reduce the amount of amylose molecules leached from potato starch and loosen the three-dimensional (3D) network structure of starch gel. This study could provide theoretical evidence for utilization of anthocyanins in diabetic-management function food.

Automated Grasp Labeling and Detection Framework with Pixel-Level Precision

Due to the differences in shape, material, and color of objects, the detection of planar grasps by robots remains challenging. Traditional methods rely on discrete grasp configurations for annotation, ignoring many possible grasp configurations. This leads to poor network generalization, making it challenging to handle a diverse range of grasped objects. Manual reannotation to continuous labels can effectively address this issue but comes with a significant cost. Therefore, this paper proposes a Pixel-level Grasp framework. Firstly, APGLG can automatically generate pixel-level grasp labels and discrete labels into continuous labels, effectively increasing the information content of individual data and improving the network generalization performance. Then, we propose the Max-Grasp-Net, built on the Multi-axis Vision Transformer and Dynamic Convolution Decomposition, to construct a U-shaped Network structure. A grasp decoder is incorporated, and deep supervision is applied to enhance network generalization. On the Cornell dataset, we achieve the best results, a grasping accuracy of 99.55%, an average success rate of 97.92% in single-object grasping, and 95.83% in multi-object grasping. We verified the effectiveness of our label generation algorithms and network innovation through actual grasp experiments.

Experimental West Nile virus infection provides lessons for recovery from enteric neuropathies.

Loss of enteric neurons leading to long-term gastrointestinal dysfunction is common to many diseases, and the path to functional recovery is unclear. In this issue of the JCI, Janova et al. report that West Nile virus killed enteric neurons and glia via CD4+ and CD8+ T cells acting through the perforin and Fas ligand pathways. Enteric glial cells contributed to neurogenesis and at least partial replacement of affected neurons. While neurogenesis is important for recovery, dysmotility and disruptions to the network structure persisted. Following enteric injury, the contribution of neurogenesis and the conditions that support restoration of enteric neural circuits for functional recovery remain for further investigation.

Optimization of territorial ecological space under the constraint of ecosystem service externalities

Ecosystem services are dynamic, following specific paths between regions and generating externalities. However, research on these externalities and the mechanisms within ecological networks remains limited. The quantification, spatial patterns, and evolution of these externalities are still unclear and need further study. Utilizing land use, topography, precipitation, and socio-economic data from 2010 and 2020, the study aims to quantify the externalities of ecosystem services and analyze their spatial distribution and network transmission characteristics within the Territorial Ecological Space (TES) of Jintan District. Using the InVEST model, we assessed the externalities of ecosystem services and integrated these with an ecological network model to reveal the impact of network structure on the flow of externalities. We propose an ecological space optimization plan with various scenarios, offering a reference for regional ecological space optimization. Key findings indicate that ecosystem service externalities exist and display spatial differentiation, with ecological source areas and corridors participating in the flow and dissipation of these services. Based on these findings, an ecological space optimization plan is proposed, consisting of multiple scenarios, which offers a reference for regional ecological space optimization. The study contributes to the sustainable management of ecological spaces by providing new insights and methodological advancements, particularly in the context of optimizing ecological spatial structures and ensuring regional ecological security. By elucidating the spatial dynamics and network transmission of ecosystem service externalities, this research supports informed decision-making in ecological conservation and land use planning.

How do network embeddedness and knowledge stock influence collaboration dynamics? Evidence from patents

Science, technology, and innovation are becoming increasingly collaborative, prompting concerted efforts to understand and measure the factors influencing these collaborations. This study aims to explore the driving factors and underlying mechanisms of collaboration dynamics based on patent data. Multilayer longitudinal networks are constructed to scrutinize interactions among organizations as well as the embedding of their knowledge elements in the network fabric. We then analyze the structures and characteristics of collaboration and knowledge networks from global and local perspectives, in which process topological indicators and graphlets are used to feature each organization's collaborative patterns and knowledge stock. Knowledge elements are extracted to present the core concepts of patents, overcoming the limitations of predefined categorizations, such as IPC, when representing technological content and context. By performing a longitudinal analysis using a stochastic actor-oriented model, we integrate network structures, node characteristics, and different dimensions of proximity to model collaboration dynamics and reveal the driving factors behind them. An empirical study in the field of lithography finds that organizations with a larger number of partners or a higher number of annular graphlets in their collaboration networks are less likely to collaborate with others. If an assignee has a more extensive range of knowledge elements and demonstrates a higher capability for knowledge combination, or if its local knowledge network exhibits weaker connectivity, its propensity to seek new collaborators increases. Both cognitive and organizational proximity play important roles in fostering collaboration.

Disentangled feature fusion network for lightweight image super-resolution

Recently, the quality of generated images in image super-resolution (SR) has significantly improved due to the widespread application of convolutional neural networks. Existing super-resolution methods often overlook the importance of network width and instead prioritize designing deeper network structures to improve performance. Deeper networks, however, demand greater computational resources and are more challenging to train, making them less suitable for devices with limited performance. To address this issue, we propose a novel method called the disentangled feature fusion network (DFFN). Specifically, we construct dual feature extraction streams (DFES) using the strategies of feature disentanglement and parameter sharing, which increase the network's width without increasing the number of parameters. To facilitate the interaction of information between the dual feature extraction streams, we design a feature interaction module (FIM) that separately enhances high and low-frequency features. Furthermore, a feature fusion module (FFM) is presented to efficiently fuse different levels of high and low-frequency feature information. The proposed DFFN integrates DFES, FIM, and FFM, which not only widens the network without increasing parameters but also minimizes the loss of crucial feature information. Extensive experiments on five benchmark datasets demonstrate that our method significantly outperforms other state-of-the-art lightweight super-resolution methods, achieving a superior balance between parameter quantity, reconstruction performance, and model complexity. The code is available at https://github.com/zhoujy-aust/DFFN.

Determination of seven phenoxy carboxylic acid herbicides in water using dispersive solid phase extraction coupled with ultra-high performance liquid chromatography-tandem mass spectrometry based on metal-organic framework composite aerogel

Phenoxy carboxylic acid herbicides (PCAs) are difficult to degrade and, thus, pose significant threats to the environment and human health. The limit for 2,4-dichlorophenoxyacetic acid is 30 μg/L in China's standards for drinking water quality, 70 μg/L in the United States' drinking water standards, and 30 μg/L in the World Health Organization's guidelines for drinking water quality. Therefore, the development of an effective detection method for trace PCAs in water is a crucial endeavor. Metal-organic frameworks (MOFs) are novel porous materials that possess advantages such as a large specific surface area, adjustable pore size, and abundant active sites. They exhibit excellent adsorption capability for various compounds. However, the applications of MOFs as adsorbents are limited. For example, the process of isolating powdered MOFs from aqueous solutions is laborious, and microporous MOFs exhibit limited surface affinity, which decreases their mass transfer efficiency in the liquid phase. MOF crystals can be embedded in a substrate to overcome these limitations. Aerogels are obtained by drying hydrogels, which are hydrophilic polymers with a three-dimensional crosslinked network structure. Spongy aerogel materials exhibit unique structural properties such as high porosity, large pore volume, ultralow density, and easy tailorability. When MOFs are combined with an aerogel, their efficient and selective adsorption properties are preserved. In addition, MOF aerogels exhibit a hierarchical porous structure, which enhances the affinity and mass transfer efficiency of the MOF for target molecules. At present, MOF aerogels are primarily prepared by freeze-drying or using supercritical carbon dioxide. These drying processes require significant amounts of energy and time. Hence, the development of greener and more efficient methods to prepare skeleton aerogels is urgently needed. In this study, we prepared an environment-friendly aerogel at ambient temperature and pressure without the use of specialized drying equipment. This ambient-dried MOF composite aerogel was then used for the dispersive solid phase extraction (DSPE) of seven PCAs from environmental water, followed by ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS). The key parameters affecting the efficiency of DSPE, including the extraction conditions, ratio of MIL-101(Fe)-NH2 to sodium alginate, pH of the aqueous samples, extraction time, ionic strength (salinity), and elution conditions, such as the elution solvent ratio, elution time, and elution volume, were investigated to obtain optimal extraction efficiency. The adsorbent could adsorb the target contaminants within 12 min, and the analytes could be completely desorbed within 30 s by elution with 4 mL of 1.5% (v/v) formic acid in methanol solution. The water samples could be analyzed without pH adjustment. The main adsorption mechanisms were electrostatic interactions and π-π conjugation. Thus, a new method based on MOF aerogels coupled with UHPLC-MS/MS was developed for the determination of the seven PCA residues in water. The calibration curves for the seven PCAs showed good linearity (r2≥0.9986), with limits of detection (LODs) and quantification (LOQs) ranging from 0.30 to 1.52 ng/L and from 1.00 to 5.00 ng/L, respectively. Good intra- and inter-day precision values of 6.5%-17.1% and 7.4%-19.4%, respectively, were achieved under low (8 ng/L), medium (80 ng/L), and high (800 ng/L) spiking levels. The developed method was applied to the detection of PCAs in surface water, seawater, and waste leachate, and the detected mass concentrations ranged from 0.6 to 19.3 ng/L. Spiked recovery experiments were conducted at mass concentrations of 8, 80, and 800 ng/L, and the recoveries ranged from 61.7% to 120.3%. The proposed method demonstrates good sensitivity, precision, and accuracy, and has potential applications in the detection of trace PCAs in environmental water.