Hexuan Hu

Generative transport models learn to move samples from one distribution to another, for example from a patient's magnetic resonance image to a computed tomography (CT) scan or from noise to an image. Recent ones, such as flow and bridge models, are used widely in medical imaging, and most of these studies report faster sampling or higher image scores than diffusion. In medicine these measures are incomplete; a synthetic CT scan, for example, must also keep the patient's anatomy and support dose calculation. We therefore review 383 studies of flow and bridge models published from 2023 to 2026, identified by a systematic search, with comparison samples of 55 diffusion and 17 normalizing-flow studies, and compare them across clinical tasks on model design and evaluation endpoints. We find that a model's medical value is shaped by the path the patient's information takes to the output, more than by whether the transport is a diffusion, flow or bridge variant. We present these models as settings of one transport viewpoint with six design axes. We then review four task families, reconstruction, translation, generation and analysis, and distil fifteen observations on the information a model receives and on how well its metrics and comparisons support its claims. For each clinical use we identify the endpoint it requires, which conventional metrics do not replace, together with the datasets and sampling costs a fair comparison needs, and give a procedure for choosing and testing a model. A continually updated reading list is maintained at https://github.com/chqwer2/Awesome-Flow-Based-Medical-Imaging.

In the digital economy, data have become a new factor of production, making a rigorous and feasible data asset valuation system a practical necessity. As the supply chain sector advances toward digitalization and intelligent transformation, data assets have become a key source of competitive advantage for supply chain companies. Based on the modified cost approach, this study develops a valuation system comprising three subsystems: cost reconstruction, return enhancement, and value adjustment. First, data asset input costs are aggregated and converted into replacement costs. The cost–expense profit ratio is then introduced to estimate a theoretical benchmark value incorporating operating returns. Four groups of influencing factors—data quality and scale, the data asset market, data asset application, and value realization risk—are incorporated. The analytic hierarchy process (AHP) and fuzzy comprehensive evaluation (FCE) are combined to derive a value adjustment coefficient. Finally, the coefficient is applied to the theoretical benchmark value to obtain the data asset value. The system is applied to Company G, a supply chain company in China, as a case study. The results show that the replacement cost is RMB 1,804,126, the value adjustment coefficient is 0.8371, and the final estimated value is RMB 1,529,111.80. Sensitivity analysis reveals that direct and indirect costs exhibit the highest sensitivity coefficients of 0.566 and 0.434, respectively, followed by data quality and scale and data asset application. This study contributes to the methodological development of differentiated data asset valuation from a value formation perspective and provides practical insights for identifying data asset management priorities in supply chain companies.

Brain-controlled robotic hand rehabilitation systems based on motor intention recognition have been used to promote recovery of hand function in stroke patients. However, the low decoding accuracy of motor imagery (MI) and unclear neural response mechanisms limit its widespread application. This study introduces a novel vibrotactile-assisted brain-controlled soft robotic hand rehabilitation system to validate its effectiveness in activating the motor sensory areas of the brain and to explore the neural response mechanisms of vibration stimulation in hand rehabilitation training. A total of 23 healthy subjects and 5 stroke patients were recruited to perform EEG and fNIRS-based experiments. Healthy subjects performed an EEG-based active rehabilitation task and an fNIRS-based passive rehabilitation task driven by the soft glove. Stroke patients only completed an EEG-based passive rehabilitation task. All experiments were conducted under two conditions: with and without vibrotactile stimulation. EEG results revealed that vibration stimulation significantly enhanced motor-sensory cortex activation during MI, and improved the online decoding performance of subjects with poor training outcomes. Grasping and stretching movements driven by the soft glove effectively activated the subjects’ motorsensory cortex. Vibration stimulation boosted the event-related desynchronization (ERD) phenomenon in the contralateral somatosensory cortex of the healthy subjects, but was not significant in the motor cortex. Meanwhile, it strengthened bilateral sensorimotor activation in stroke patients. Moreover, fNIRS results indicated that vibration stimulation increased the concentration of HbO in the motor-sensory areas during passive movement and enhanced the bidirectional functional connectivity between the left and right hemispheres. These findings suggest that the proposed tactile-assisted hand rehabilitation system can effectively enhance neural activation in the motor-sensory cortex, potentially leading to improved hand function recovery in stroke patients.

Accurate vessel trajectory prediction is vital for forecasting navigation trends, reducing potential risks, and ensuring maritime traffic safety. Using automatic identification system (AIS) data to improve the comprehensive performance of vessel future trajectory prediction is an urgent problem in intelligent transportation systems. In this paper, an improved Pelican optimization algorithm (IPOA) optimized bidirectional gated recurrent unit-attention mechanism (BiGRU-AM) vessel trajectory prediction framework based on AIS data is proposed. This method integrates the optimized hyperparameters of IPOA into the BiGRU-AM backbone network. Utilizing the BiGRU-AM network with tuned parameters to achieve bidirectional information enhancement, and establish an efficient IPOA-BiGRU-AM hybrid prediction model. Furthermore, we propose a shared fusion training mechanism (SFTM) for information exchange between modules. A comprehensive evaluation system covering global error, local error and spatial similarity is developed to address the limitations of single indicator evaluation. The trajectory prediction effectiveness of this method is verified on three datasets. Compared with the current methods, this approach improves accuracy by up to 79.30% across all indicators and reduces the average prediction error by 76.31%. In terms of model complexity, our model’s running speed has improved by 49.73% on average, and has a 1.5 times computational advantage compared to baseline models. The experimental results proved the superiority of the proposed method.

Crowd counting aims to estimate the number, density, and distribution of crowds in an image. While CNN-based crowd counting methods have been effective, head-scale variation and complex background remain two major challenges for crowd counting. Therefore, we propose a multiscale region calibration network called MRCNet to effectively address these challenges. To address the former challenge, we design a multiscale aware module that utilizes multi-branch dilated convolutional parallelism to obtain multiscale receptive fields and cope with drastic changes in head size. For the latter challenge, we design a regional calibration module that calibrates the attention weights of each region after obtaining the attention map to effectively handle challenges in complex contexts. Additionally, we improve the loss function by combining L2 loss and binary cross-entropy loss to help MRCNet achieve excellent results. Extensive experiments were conducted on three mainstream datasets to demonstrate the robustness and competitiveness of our approach.

Efficient bearing fault diagnosis not only extends the operational lifespan of rolling bearings but also reduces unnecessary maintenance and resource waste. However, current deep learning-based methods face significant challenges, particularly due to the scarcity of fault data, which impedes the models’ ability to effectively learn parameters. Additionally, many existing methods rely on single-scale features, hindering the capture of global contextual information and diminishing diagnostic accuracy. To address these challenges, this paper proposes a Multi-Scale Convolutional Neural Network with Self-Knowledge Distillation (MSCNN-SKD) for bearing fault diagnosis. The MSCNN-SKD employs a five-stage architecture. Stage 1 uses wide-kernel convolution for initial feature extraction, while Stages 2 through 5 integrate a parallel multi-scale convolutional structure to capture both global contextual information and long-range dependencies. In the final two stages, a self-distillation process enhances learning by allowing deep-layer features to guide shallow-layer learning, improving performance, especially in data-limited scenarios. Extensive experiments on multiple datasets validate the model’s high diagnostic accuracy, computational efficiency, and robustness, demonstrating its suitability for real-time industrial applications in resource-limited environments.