Search Results (950)

Background/Objectives: Portable and wearable ECG technologies are increasingly used in adult cardiac monitoring. However, evidence supporting their feasibility and diagnostic performance in pediatric populations remains limited. This systematic review evaluates the diagnostic accuracy, usability, artifact susceptibility, and user acceptance of mobile ECG technologies in pediatric cardiology. Methods: A systematic literature search was performed in the Embase, PubMed, Scopus, and Web of Science databases. The review was conducted in accordance with the PRISMA 2020 guidelines and was registered in the PROSPERO database. Results: A total of 30 publications were included in the final analysis. Portable ECG devices demonstrated good feasibility diagnostic utility in children. Handheld systems provided high-quality tracings with strong agreement with standard 12-lead ECGs and higher adherence, as well as user satisfaction compared with conventional event recorders. However, automated rhythm classification frequently misidentified pediatric arrhythmias. Smartwatch-based ECG recordings showed high diagnostic accuracy when manually interpreted, but automated algorithms were unreliable, particularly for tachyarrhythmias and conduction abnormalities. Alternative electrode placement strategies improved smartwatch performance, and patient acceptance was consistently high. ECG patch monitoring, particularly with extended-wear devices, achieved the highest diagnostic yield, detecting arrhythmias often missed by short-duration Holter monitoring while maintaining comparable signal quality. Conclusions: Mobile ECG technologies represent a promising adjunct for pediatric rhythm surveillance, offering diagnostic performance comparable to standard modalities when interpreted by clinicians and improved usability and patient acceptance. Persistent limitations include the poor reliability of adult-oriented automated algorithms and the underrepresentation of younger children and the predominantly off-label use of these devices in pediatric populations, underscoring the need for pediatric-specific algorithm development and age-adapted device design. Full article

Background and Objectives: Visceral artery aneurysms (VAAs) are rare but potentially catastrophic vascular abnormalities, particularly in pregnant patients or women of childbearing age. Rupture is often fatal for both mother and fetus, with mortality rates exceeding 70% in some series. While most VAAs are found incidentally, a subset may present acutely with nonspecific abdominal or flank pain, making early recognition and appropriate referral essential. This review article aims to provide General Practitioners (GPs) and emergency department (ED) clinicians with a practical approach to the recognition, investigation, initial management, and escalation pathways for VAAs. Results: Physiological and hormonal adaptations in pregnancy heighten aneurysm rupture risk. Despite this, imaging is frequently delayed. Computed tomography angiography (CTA) remains the gold standard for diagnosis and is safe in pregnancy when clinically justified, with fetal radiation exposure well below teratogenic thresholds. Guidelines from major vascular societies uniformly recommend repairing VAAs in pregnancy or women planning pregnancy irrespective of aneurysm size, and treating pseudoaneurysms urgently in all patients. Endovascular intervention is first-line where anatomy permits, while open or hybrid approaches remain essential in unstable presentations. The manuscript outlines practical steps for ED and GP settings, including haemodynamic stabilization, early obstetric involvement, transfer considerations for rural environments, reproductive counselling, and post-repair surveillance. Conclusions: With an increasing number of abdominal scans being performed in primary and tertiary settings, there is an associated increased volume of incidental findings that require work-up. This article outlines a practical investigation and management strategy for clinicians presented with VAAs, including in high-risk cohorts, emphasizing early imaging, inter-specialty coordination, and guideline-supported thresholds for intervention. Full article

Temporomandibular disorders (TMDs) are multifactorial conditions traditionally attributed to excessive mechanical loading on the temporomandibular joint, leading to clinical manifestations ranging from joint sounds to structural deformation. Contributing factors include trauma, occlusal abnormalities, psychological stress, and bruxism. However, immune and molecular alterations associated with early disease activity are not systematically integrated into structure-centered TMD frameworks. Emerging evidence indicates that temporomandibular joint osteoarthritis (TMJOA) involves activation of innate immunity caused by damage-associated molecular patterns (DAMPs) generated through mechanical loading, together with non-antigen-specific adaptive immune responses, including macrophage polarization and T helper 17 (Th17) and regulatory T (Treg) cell imbalance. Inflammatory and mechanical inputs converge through shared signaling modules and mechanoresponsive transcriptional programs, promoting extracellular matrix degradation, fibrotic remodeling, and subchondral bone remodeling. This review synthesizes the current immunopathological and mechanobiological evidence and introduces temporomandibular immunologic disease (TMID) as a mechanism-oriented framework, characterized by a reinforcing cycle between mechanically induced tissue damage and immune activation within the temporomandibular joint (TMJ) microenvironment. TMID complements TMJOA and Diagnostic Criteria for Temporomandibular Disorders (DC/TMD) structural diagnostic categories while excluding antigen-specific autoimmune arthritides such as rheumatoid arthritis, thus functioning as a mechanistic overlay framework for the integration of immuno-mechanical signaling networks in immune-active, mechanically driven TMJ pathology. Full article

Objective: This study aims to investigate the protective effect of Shengmai San (SMS) against high-glucose (HG)-induced injury in neonatal rat ventricular myocytes (NRVMs) and to elucidate the underlying pharmacological molecular mechanisms. We hypothesize that SMS ameliorates HG-induced calcium homeostasis imbalance in NRVMs by improving mitochondrial energy metabolism disorder, and this protective effect is associated with the downregulation of oxidized and phosphorylated CaMKII expression to inhibit CaMKII signaling pathway overactivation. Herein, we verify this hypothesis by assessing mitochondrial function, calcium transients, sarcoplasmic reticulum (SR) calcium handling and CaMKII phosphorylation levels in NRVMs. Methods: First, ultra-high performance liquid chromatography–high resolution mass spectrometry was used to identify the chemical components of SMS to clarify its material basis. Primary NRVMs were then cultured under low-glucose (LG) or HG conditions, with 2% SMS-medicated serum (SMS-MS) as the experimental intervention, and NAC (ROS scavenger) and KN93 (CaMKII inhibitor) as positive controls. Following intervention, we sequentially detected key indicators corresponding to the proposed pathological pathway: intracellular reactive oxygen species (ROS) levels (oxidative stress), mitochondrial ROS, mitochondrial function indices including oxygen consumption rate (OCR) (energy metabolism), calcium transients and diastolic intracellular free calcium concentration (global calcium homeostasis), sarcoplasmic reticulum (SR) calcium leak (calcium handling disorder), and, finally, the phosphorylation, oxidation levels of CaMKII and RyR2 phosphorylation (Ser2814) (p-RyR2) (key regulatory pathway) via Western blot to systematically elucidate the mechanistic link between SMS intervention and HG-induced NRVM injury. Results: Quantitative analysis revealed that high-glucose (HG) induction significantly reduced calcium transient amplitude and prolonged the decay time constant (tau) in NRVMs at 72 h (p < 0.01 vs. LG), with these parameters normalizing by 120 h—an effect indicative of a compensatory adaptive response. The 2%SMS-MS markedly ameliorated HG-induced calcium transient abnormalities at 72 h (p < 0.01 vs. HG). Additionally, 2%SMS-MS significantly enhanced mitochondrial basal oxygen consumption rate, spare respiratory capacity, ATP production, and maximal respiration in HG-exposed NRVMs (p < 0.01 vs. HG). SMS also significantly reduced intracellular reactive oxygen species (ROS) levels (p < 0.01 vs. HG), mitochondrial ROS levels (p < 0.01 vs. HG), diastolic intracellular free calcium concentration (p < 0.01 vs. HG), and SR calcium leak (p < 0.05 vs. HG). Western blot analysis revealed that 2%SMS-MS intervention effectively downregulated the expression of oxidized CaMKII (Ox-CaMKII) (p < 0.01 vs. HG), phosphorylated CaMKII (p-CaMKII) (p < 0.01 vs. HG), and RyR2 phosphorylation (Ser2814) (p < 0.05 vs. HG), which may be the potential mechanism in maintaining calcium homeostasis in HG-induced NRVMs. Conclusions: This study suggests that SMS enhances mitochondrial energy metabolism and exerts a protective effect against high-glucose-induced calcium homeostasis imbalance in NRVMs, which supports our proposed hypothesis. Its potential mechanism indicates that the protective effects of SMS are associated with its ability to downregulate the expression of oxidized and phosphorylated CaMKII. These findings highlight SMS as a potential therapeutic candidate for alleviating HG-related myocardial injury and provide evidence for its application in the prevention of early diabetic cardiomyopathy. Full article

Urban underground sewage pipelines often suffer from defects such as cracks, irregular joint misalignment, and stratified sedimentation blockages, which may lead to pipeline bursts, sewage overflow, and water pollution. Timely detection of abnormal defects in sewage pipelines is critical to ensuring public health and environmental sustainability. Vision-based sewage pipeline defect detection plays a crucial role in modern urban wastewater treatment systems. However, it still faces challenges such as limited feature extraction capabilities, insufficient multi-scale defect characterization, and poor positioning stability when dealing with low-contrast images and in environments with severe background interference. To address this issue, this study proposes an enhanced SAW-YOLOv8l model that integrates RT-DETR (real-time detection Transformer) with CNN (convolutional neural network) architecture. First, a C2f_SCA module improves the long-distance feature extraction capability and localization precision. Second, an AIFI-PRBN module enhances global feature correlation through attention-mechanism-based intra-scale feature interaction and reduces computational complexity using lightweight techniques. Finally, an adaptive dynamic weighted loss function based on Wise-IoU (weighted intersection over union) further improves training convergence and robustness by balancing the gradient distribution of samples. Experiments on a mixed dataset comprising Sewer-ML and industrial images demonstrate that the SAW-YOLOv8l model achieved mAP@0.5 of 86.2% and precision of 84.4%, which were improvements of 2.4% and 6.6% respectively over the baseline model, significantly enhancing the detection performance of abnormal defects in sewage pipelines. Full article

This study proposes an improved retinal blood vessel segmentation method to enhance the diagnosis of microvascular retinal complications. The proposed method extracts local shape features from retinal images utilizing a fractional Hessian matrix, which models blood vessels as surface structures characterized by ridges and valleys resulting from variations in curvature. The methodology integrates adaptive principal curvature estimation with a new framework leveraging the fractional Hessian matrix with nonsingular and nonlocal kernels. The effectiveness of the suggested method is assessed using publicly accessible datasets, including DRIVE, HRF, STARE, and some real images obtained from a local hospital. The proposed segmentation achieves 96.77% accuracy and 98.82% specificity on the DRIVE database, 96.91% accuracy and 98.69% specificity on STARE, and 95.90% accuracy and 98.36% specificity on the HRF database. Optimal parameters for the fractional order and Gaussian standard deviation were empirically determined by maximizing segmentation accuracy. Our findings show that the proposed approach achieves competitive performance compared to the listed methods, including several deep learning approaches, while maintaining significant computational efficiency. The output of the suggested method can be further utilized with deep learning techniques, which will be applied in the clinical context of diabetic retinopathy and glaucoma to identify abnormalities likely related to disease progression and different stages. Full article

Unsupervised video anomaly detection (VAD) methods learn from normal data to identify anomalies by capturing pattern deviations. However, they often struggle to model multi-scale features and distinguish between normal and abnormal instances. To address these limitations, we propose a Multi-scale U-shaped Adaptive Clustering Learning (MS-UACL) framework. Built on the U-Net architecture, we redesign it as a 3D-encoder/2D-decoder autoencoder. In the encoder, we introduce a Dual-scale Feature Cascading Module (IDCN), which adopts a pseudo-branch fusion mechanism to systematically model multi-scale spatiotemporal features, thereby enhancing the model’s representational capability. To further enhance the distinction between normal and anomalous patterns, we propose an MLP-based Adaptive Clustering Algorithm (MLP-ACA). Specifically, MLP-ACA employs an initial mapping mechanism to align cluster centers with the underlying normal data distribution, facilitating more accurate feature reconstruction. Additionally, we introduce an adaptive clustering update strategy that optimizes cluster centers by tuning solely the parameters of the MLP. This enables the cluster centers to autonomously converge toward optimal feature representations, thereby accelerating clustering convergence and enhancing pattern separability. Extensive experiments on three benchmark datasets demonstrate that the proposed MS-UACL framework outperforms most existing methods on small- and medium-scale datasets. Full article

In harsh marine environments, vessel energy storage systems (VESS) face elevated thermal runaway (TR) risk, yet practical early warning remains difficult because early voltage differences between TR high-risk cells and normal cells are often weak, warning thresholds vary across operating segments, and decisions relying on a single feature are prone to false or missed warnings. To overcome these difficulties, this study develops a four-part early warning strategy for TR high-risk cells in VESS. First, the original cell voltages are denoised through multiscale jump plus mode decomposition and Spearman correlation guided mode reconstruction to suppress irrelevant interference. Second, an improved Sigmoid nonlinear mapping is introduced to enhance subtle inter-cell voltage deviations and improve early separability. Third, sparse representation is used to construct a cell deviation score, and an adaptive threshold is employed to perform primary abnormal-cell screening under varying segment conditions. Finally, multidimensional mutual information value derived from voltage, temperature, and their rates of change is incorporated into a joint assessment methodology to further verify the abnormal state of flagged cells. Validation on 18 independent real operation cases comprising 2483 discharge segments shows that, across the evaluated TR high-risk cases, the shortest confirmed warning lead time achieved by the proposed strategy was 14 days. The proposed strategy also reduced false and missed warnings, outperformed the compared benchmark methods overall, and retained computational feasibility for onboard application in VESS. Full article

Few-shot industrial anomaly detection technology has received more and more attention because it does not require a large number of abnormal samples to train. Recent few-shot industrial anomaly detection methods commonly fuse multi-stage features from frozen vision transformers for anomaly scoring. However, we find that such direct fusion suffers from cross-stage representation misalignment—shallow and deep features differ significantly in scale and semantic granularity, leading to inconsistent anomaly maps and degraded localization. To address this problem, we propose Calibrated Feature Fusion (CFF), a lightweight adapter that enhances feature fusion via cross-stage representation alignment. The CFF module can be integrated into existing state-of-the-art frameworks and operates effectively in few-shot settings. Experiments on MVTec AD and VisA show that CFF consistently improves the state-of-the-art method across 1/2/4-shot settings, achieving gains of up to +1.6% AUROC and +4.1% AP in pixel-level segmentation. Notably, CFF enhances both precision and recall in four-shot scenarios. Ablation studies confirm that cross-stage alignment is key to stable multi-stage fusion. Full article

Background: The recognition of autism spectrum disorder (ASD) has been a challenge due to the heterogeneity in symptoms and complex variations in brain function. Resting-state functional magnetic resonance imaging (rs-fMRI) has become instrumental in studying these disorders by accessing underlying abnormal neural activity and connectivity. Recently, deep learning approaches have shifted the analysis of brain networks by capturing spatiotemporal information from fMRI sequences. Nonetheless, most existing studies are limited by relying on a single representational scale, typically restricting analysis to either voxel-level spatiotemporal patterns or static connectivity matrices. Additionally, the dynamic reconfiguration of functional coupling and its variations across different anatomical parcellations are often ignored, which obscures neurobiologically meaningful dynamics. Methods: In this regard, we propose a multi-atlas dynamic connectivity transformer fused with 4D spatiotemporal modeling for ASD recognition (MADCT-4D). Specifically, the framework comprises two complementary branches. The 4D spatiotemporal branch encodes raw rs-fMRI volumes to learn hierarchical representations of evolving neural activity, while the dynamic-connectivity branch models time-resolved functional connectivity sequences constructed from multiple atlases, enabling the network to capture dynamic reconfiguration at the connectome level under different parcellation granularities. Moreover, we perform late fusion by combining the branch-specific decision scores with a learnable gate, allowing the model to adaptively weight voxel-level dynamics and multi-atlas connectivity evidence for each subject. Results: Extensive experiments on the publicly available ABIDE dataset demonstrate that the proposed method achieves 90.2% accuracy for ASD recognition, outperforming multiple competitive baselines. Conclusions: The proposed framework yields interpretable biomarkers based on learned dynamic connectivity patterns that are consistent with altered functional coupling in ASD. Full article

Against the global carbon neutrality target, predictive maintenance (PdM) of automotive engines represents a core technical strategy to advance the sustainable development of the automotive industry. Conventional single-modal diagnostic approaches for engine abnormal sound defects suffer from low accuracy and weak anti-interference capability. Existing multi-modal fusion methods fail to deeply mine the physical coupling between cross-modal features and often entail excessive model complexity, hindering deployment on resource-constrained on-board edge devices. To resolve these limitations, this study proposes a Physical Prior-Embedded Cross-Modal Attention (PPE-CMA) mechanism for lightweight multi-modal fusion diagnosis of engine abnormal sound defects. First, wavelet packet decomposition (WPD) and mel-frequency cepstral coefficients (MFCC) are integrated to extract time-frequency features from engine audio signals, while a channel-pruned ResNet18 is employed to extract spatial features from engine thermal imaging and vibration visualization images. Second, the PPE-CMA module is designed to adaptively assign attention weights to audio and image features by exploiting the physical coupling between engine fault acoustic and visual characteristics, enabling efficient cross-modal feature fusion with redundant information suppression. A rigorous theoretical derivation is provided to link cosine similarity with the physical correlation of engine fault acoustic-visual features, justifying the attention weight constraint (β = 1 − α) from the perspective of fault feature physical coupling. Third, an improved lightweight XGBoost classifier is constructed for fault classification, and a hybrid data augmentation strategy customized for engine multi-modal data is proposed to address the small-sample challenge in industrial applications. Ablation experiments on ResNet18 pruning ratios verify the optimal trade-off between diagnostic performance and computational efficiency, while feature distribution analysis validates the authenticity and effectiveness of the hybrid augmentation strategy. Experimental results on a self-constructed multi-modal dataset show that the proposed method achieves 98.7% diagnostic accuracy and a 98.2% F1-score, retaining 96.5% accuracy under 90 dB high-level environmental noise, with an end-to-end inference speed of 0.8 ms per sample (including preprocessing, feature extraction, and classification). Cross-engine and cross-domain validation on a 2.0T diesel engine small-sample dataset and the open-source SEMFault-2024 dataset yield average accuracies of 94.8% and 95.2%, respectively, demonstrating strong generalization. This method effectively enhances the accuracy and robustness of engine abnormal sound defect diagnosis, offering a lightweight technical solution for on-board real-time fault diagnosis and in-plant online quality inspection. By reducing engine fault-induced energy loss and spare parts waste, it further promotes energy conservation and emission reduction in the automotive industry. Quantified experimental data on fuel efficiency improvement and carbon emission reduction are provided to substantiate the ecological benefits of the proposed framework. Full article

Access control systems rely increasingly on multimodal biometric and behavioral signals to enhance security and robustness against sophisticated attacks. However, when heterogeneous modalities provide conflicting evidence, such as valid biometric credentials accompanied by abnormal behavioral or acoustic patterns, traditional fusion strategies based on static thresholds or majority voting often fail, leading to false alarms or insecure authorization decisions. This paper addresses this critical limitation by proposing a contextual decision-making fusion framework designed to resolve conflicting multimodal evidence at the decision-making level. The proposed approach models access control as a decision-making problem in a context of uncertainty, where independent agents generate modality-specific evidence from authentication channels based on face, voice, and fingerprints. A centralized fusion mechanism integrates heterogeneous results using adaptive reliability weighting and contextual reasoning to resolve conflicts before operational decisions are made. Rather than treating each modality independently, the framework explicitly considers inconsistencies, uncertainties, and situational context when aggregating evidence. The framework is evaluated using public benchmarks, including VGGFace2, VoxCeleb2, and FVC2004, combined with controlled multimodal scenarios that induce conflicting evidence. Experimental results obtained under controlled contradiction scenarios show that the proposed fusion strategy reduces false alarms and improves decision consistency by approximately 18%. These results are interpreted within the scope of controlled multimodal simulations. Full article

In abnormal goat lung sound recognition, high inter-class similarity and large intra-class variability pose significant challenges. To address this issue and improve recognition performance, we propose a deep learning model, AAF-SwinT, based on an improved Swin Transformer. The model replaces the original Swin Transformer self-attention module with Axial Decomposed Attention (ADA), modeling the temporal and frequency axes separately and integrating attention weights to mitigate inter-class feature similarity. Adaptive Spatial Aggregation for Patch Merging (ASAP) is designed to emphasize key time-frequency regions, and a Frequency-Aware Multi-Layer Perceptron (FAM) is introduced to model features across different frequency bands, further enhancing the discriminative ability for abnormal lung sounds. Experiments on a self-constructed goat lung sound dataset demonstrate that AAF-SwinT achieves an accuracy of 88.21%, outperforming existing mainstream Transformer-based models by 2.68–5.98%. Ablation studies further confirm the effectiveness of each proposed module, improving the accuracy of baseline Swin Transformer model from 85.53% to 88.21%. These results indicate that the proposed approach exhibits strong robustness and practical potential for abnormal lung sound recognition in goats, providing technical support for early diagnosis and management of respiratory diseases in large-scale goat farming. Full article

Background/Objectives: Abnormal muscle tone and impaired motor control commonly limit gait recovery after stroke. Robot-assisted gait training has been introduced to augment conventional rehabilitation; however, its effects on stage-based motor recovery, functional ambulation, and muscle tone during the subacute phase remain unclear. Methods: This prospective, single-center, randomized controlled trial enrolled 30 patients with subacute stroke who received robot-assisted gait training plus conventional rehabilitation (R-BoT Plus group, n = 15) or conventional rehabilitation alone (control group, n = 15) over 4 weeks. The primary outcome was the change in Brunnstrom recovery stage of the lower extremities (BRS-LE). Secondary outcomes included Functional Ambulation Category (FAC), Fugl–Meyer Assessment for the Lower Extremity (FMA-LE), clinical spasticity measures (Modified Ashworth Scale and Modified Tardieu Scale), and muscle mechanical properties (MyotonPRO). Exploratory analyses were conducted to examine the associations between changes in stage-based motor recovery (ΔBRS-LE), functional ambulation (ΔFAC), and MyotonPRO parameters. Within-group changes were assessed using the Wilcoxon signed-rank test. Between-group effects were primarily evaluated using baseline-adjusted ANCOVA with HC3 robust standard errors, with Wilcoxon rank-sum tests on change scores as sensitivity analyses. Associations between changes in clinical outcomes and MyotonPRO parameters were evaluated using Spearman’s rank correlation coefficient (ρ). Results: BRS-LE (p = 0.014) and functional ambulation (p = 0.041) were significantly improved in the R-BoT Plus group. Changes in FMA-LE and clinical spasticity measures did not differ significantly between groups. Quantitative myotonometry revealed selective muscle- and parameter-specific changes. No robust correlations were observed between MyotonPRO parameters and changes in BRS-LE. Conclusions: The addition of robot-assisted gait training to conventional rehabilitation was associated with greater improvements in stage-based lower-limb motor recovery and functional ambulation in patients with subacute stroke. In contrast, cumulative impairment scores and conventional clinical spasticity measures demonstrated limited changes between groups. Quantitative muscle mechanical assessment revealed selective muscle-specific adaptations, supporting its role as a complementary tool for mechanistic characterization rather than as a surrogate marker of motor recovery. Future studies incorporating dose-matched designs and longer follow-up periods are warranted to clarify the independent and long-term effects of robot-assisted gait training. Full article

This study uses multidimensional indicators of macroeconomics, supply and demand, cost, and market microstructure to construct an intelligent framework integrated with optimized Exponentially Weighted Moving Average (EWMA) denoising for price forecasting and black early warning for egg futures in China from 2014 to 2023. Black early warning serves as a non-parametric early warning method that identifies abnormal price increases and falls based on historical fluctuation thresholds. As the first livestock future contract listed in China, accurate egg price forecasting is crucial for risk prevention and market control and regulation. First, LASSO regression was used to screen the core driving factors of egg futures prices. Nine key indicators were identified and input into the hybrid Temporal Convolutional Network–Gated Recurrent Unit (TCN-GRU) prediction model. To address the high-frequency noise in the original price series, two-dimensional optimization was performed on traditional EWMA denoising to achieve more adaptive noise filtering. By applying the black early warning method, the obtained future egg price fluctuations were more consistent with the actual situation. In addition, empirical analysis of multi-horizon forecasting and early warning for t + 1, t + 5, and t + 10 was carried out to further verify the model’s prediction accuracy. The results show that compared with the single TCN model, the single GRU model, and the TCN-GRU model without denoising, the TCN-GRU model integrated with optimized EWMA denoising achieves better prediction performance on the test set. In terms of the early warning matching rate, it reaches 83.33% for the t + 1 horizon, and the prediction accuracy for the t + 5 and t + 10 horizons decreases regularly but remains stable above 60%. In contrast, the highest early warning matching rate of the model without denoising is only 22.22% across all horizons, which has no practical early warning value. The early warning signals generated by the optimized EWMA denoising-based TCN-GRU model can effectively identify abnormal sharp rises and falls in egg futures prices, providing effective support for hedging and risk management for market participants. The study’s limitations are discussed, as well as future research directions. The findings provide a basis for decision making for agricultural producers and future investors and support the development of China’s agricultural product market. Full article