Modulation Features Research Articles

ChemXTree: A Feature-Enhanced Graph Neural Network-Neural Decision Tree Framework for ADMET Prediction.

The rapid progression of machine learning, especially deep learning (DL), has catalyzed a new era in drug discovery, introducing innovative approaches for predicting molecular properties. Despite the many methods available for feature representation, efficiently utilizing rich, high-dimensional information remains a significant challenge. Our work introduces ChemXTree, a novel graph-based model that integrates a Gate Modulation Feature Unit (GMFU) and neural decision tree (NDT) in the output layer to address this challenge. Extensive evaluations on benchmark data sets, including MoleculeNet and eight additional drug databases, have demonstrated ChemXTree's superior performance, surpassing or matching the current state-of-the-art models. Visualization techniques clearly demonstrate that ChemXTree significantly improves the separation between substrates and nonsubstrates in the latent space. In summary, ChemXTree demonstrates a promising approach for integrating advanced feature extraction with neural decision trees, offering significant improvements in predictive accuracy for drug discovery tasks and opening new avenues for optimizing molecular properties.

Diagnosis of Custard Apple Disease Based on Adaptive Information Entropy Data Augmentation and Multiscale Region Aggregation Interactive Visual Transformers

Accurate diagnosis of plant diseases is crucial for crop health. This study introduces the EDA–ViT model, a Vision Transformer (ViT)-based approach that integrates adaptive entropy-based data augmentation for diagnosing custard apple (Annona squamosa) diseases. Traditional models like convolutional neural network and ViT face challenges with local feature extraction and large dataset requirements. EDA–ViT overcomes these by using a multi-scale weighted feature aggregation and a feature interaction module, enhancing both local and global feature extraction. The adaptive data augmentation method refines the training process, boosting accuracy and robustness. With a dataset of 8226 images, EDA–ViT achieved a classification accuracy of 96.58%, an F1 score of 96.10%, and a Matthews Correlation Coefficient (MCC) of 92.24%, outperforming other models. The inclusion of the Deformable Multi-head Self-Attention (DMSA) mechanism further enhanced feature capture. Ablation studies revealed that the adaptive augmentation contributed to a 0.56% accuracy improvement and a 0.34% increase in MCC. In summary, EDA–ViT presents an innovative solution for custard apple disease diagnosis, with potential applications in broader agricultural disease detection, ultimately aiding precision agriculture and crop health management.

Science and Technology of Supercapacitor and its Applications

Super-capacitors (SCs) are significant because of their unique characteristics, which include long cycle life, high strength, and environmental friendliness. SCs use electrode substances with high specific surface area and thinner dielectrics. Referring to the energy storage mechanism, all kinds of SCs were reviewed in this review paper; a quick synopsis of the materials and technology used for SCs is provided. Materials such as conducting polymers, carbon materials, metal oxides, and their composites are the main focus. The performance of the composites was evaluated using metrics such as energy, cycle performance, power capacitance, and rate capability, which also provides information on the electrolyte materials. To precisely appraise the state of Charge (SoC) in the super SCs cell module, its identical model o is used. It is expected that this model will accurately capture the features of the cell module, specifically its standing-related self-discharge behavior, and the outcomes of parameter identification directly impact its accuracy. Engine downsizing is a result of the requirement to increase fuel efficiency and lower CO2 and other hazardous pollutant emissions from internal combustion engine cars. However, smaller turbocharged engines have a relatively poor torque capability at low engine speeds. To solve this issue, an electrical torque boost based on SCs may be used to help recover energy during regenerative braking as well as during acceleration and gear changes.

Research on underwater acoustic detection technology based on optical waveguide resonator cavity

Purpose In acoustic detection technology, optical microcavities offer higher detection bandwidth and sensitivity than traditional acoustic sensors. However, research on acoustic detection technologies involving optical microcavities has not yet been reported. Therefore, this paper aims to design and construct an underwater acoustic detection system based on optical microcavities and study its acoustic detection technology to improve its performance. Design/methodology/approach Based on the principles of optical microcavity acoustic sensors, a signal-detection circuit was designed to form a detection system in conjunction with a laser, an optical waveguide resonator and an oscilloscope. This circuit consists of two modules: a photodetection module and a filter amplification module. Findings The photodetection module features a baseline noise of −106.499 dBm and can detect device spectral line depths of up to 2410 mV. The gain stability of the filter amplification module was 58 dB ± 1 dB with a noise gain of −107.626 dBm. This design allows the acoustic detection system to detect signals with high sensitivity within the 10 Hz−1.2 MHz frequency band, achieving a maximum sensitivity of −126 dB re 1 V/µPa at 800 Hz and a minimum detectable pressure (MDP) of 0.37 mPa/Hz1/2, corresponding to a noise equivalent pressure (NEP) of 51.36 dB re 1 V/µPa. Originality/value This study designs and constructs a broadband underwater acoustic detection system specifically for optical waveguide resonators based on the sensing principles of silicon dioxide optical waveguide resonators. Experiments demonstrated that the signal detection module improves the sensitivity of underwater acoustic detection based on optical waveguides.

Infrared Image Generation Based on Visual State Space and Contrastive Learning

The preparation of infrared reference images is of great significance for improving the accuracy and precision of infrared imaging guidance. However, collecting infrared data on-site is difficult and time-consuming. Fortunately, the infrared images can be obtained from the corresponding visible-light images to enrich the infrared data. To this end, this present work proposes an image translation algorithm that converts visible-light images to infrared images. This algorithm, named V2IGAN, is founded on the visual state space attention module and multi-scale feature contrastive learning loss. Firstly, we introduce a visual state space attention module designed to sharpen the generative network’s focus on critical regions within visible-light images. This enhancement not only improves feature extraction but also bolsters the generator’s capacity to accurately model features, ultimately enhancing the quality of generated images. Furthermore, the method incorporates a multi-scale feature contrastive learning loss function, which serves to bolster the robustness of the model and refine the detail of the generated images. Experimental results show that the V2IGAN method outperforms existing typical infrared image generation techniques in both subjective visual assessments and objective metric evaluations. This suggests that the V2IGAN method is adept at enhancing the feature representation in images, refining the details of the generated infrared images, and yielding reliable, high-quality results.

Visual tracking algorithm based on template updating and dual feature enhancement

Aiming at the problem of tracking failure due to target deformation, flipping and occlusion in visual tracking, a template updating algorithm based on image structural similarity is proposed by dynamically updating the template to adapt to the changes of the target during tracking, specifically, a queue is used to save the recent N -frame tracking results, and a decision is made on whether to update the template or not based on the structural similarity score between the current tracking results and the template image, and if updated, the template is matched from the historical N -frame tracking results as the new template. If updated, the optimal target image is matched from the historical N -frame tracking results as a new template for subsequent tracking. The tracking feature enhancement module and segmentation feature enhancement module are also designed based on SiamMask network. The tracking feature enhancement module consists of non-local operations and convolutional downsampling, which is used to establish contextual correlation, enhance the target features, suppress the background interference, improve the tracking robustness, and solve the feature attenuation problem due to the occlusion of the target. The segmentation feature enhancement module introduces the convolutional block attention module and deformable convolution to improve the network's ability to capture channel and spatial features, adaptively learn the shape and contour information of the target, and enhance the network's segmentation accuracy of the tracked target. , which in turn improves the tracking accuracy. Experiments show that the proposed algorithm performs well and stably in solving the above problems, improving the expected average overlap rate by 5.2%, 5.3%, and 2.5%, and the robustness by 6%, 7.9%, and 15.6%on the VOT2016, VOT2018, and VOT2019 datasets , respectively, and achieving a real-time speed of 91 frames per second, when compared to the baseline SiamMask.

Predicting Dependent Edges in Nonequilibrium Complex Systems Based on Overlapping Module Characteristics

Problem: Predicting dependency relationships in nonequilibrium systems is a critical challenge in complex systems research. Solution proposed: In this paper, we propose a novel method for predicting dependent edges in network models of nonequilibrium complex systems, based on overlapping module features. This approach addresses the many-to-many dependency prediction problem between nonequilibrium complex networks. By transforming node-based network models into edge-based models, we identify overlapping modular structures, enabling the prediction of many-to-many dependent edges. Experimental evaluation: This method is applied to dependency edge prediction in power and gas networks, curriculum and competency networks, and text and question networks. Results: The results indicate that the proposed dependency edge prediction method enhances the robustness of the network in power–gas networks, accurately identifies supporting relationships in curriculum–competency networks, and achieves better information gain in text–question networks. Conclusion: These findings confirm that the overlapping module-based approach effectively predicts dependencies across various nonequilibrium complex systems in diverse fields.

Dense projection fusion for 3D object detection

Fusing information from LiDAR and cameras can effectively enhance the overall perceptivity of autonomous vehicles in various scenarios. Despite the relatively good results achieved by point-wise fusion and Bird’s-Eye-View (BEV) fusion, they still cannot fully leverage the image information and lack of effective depth information. For any fusion methods, the multi-modal features first need to be concatenated along the channel, and then the fused features are extracted using convolutional layers. However, this type of fusion methods is effective, but too coarse which causes that the fused features cannot pay more attention to the regions with important features and suffer from severe noise. To tackle these issues, we propose in this paper a Dense Projection Fusion (DPFusion) approach. It consists of two new modules: dense depth map guided BEV transform (DGBT) module and multi-modal feature adaptive fusion (MFAF) module. The DGBT module first quickly estimates the depth of each pixel and then projects all image features to the BEV space, making full use of the image information. The MFAF module computes the image weights and point cloud weights for each channel in each BEV grid and then adaptively weights and fuses the image BEV features with the point cloud BEV features. It is worth pointing out that the MFAF module makes the fused features pay more attention to background outline and object outline. Our proposed DPFusion demonstrates competitive results in 3D object detection, achieving a mean Average Precision (mAP) of 70.4 and a nuScenes detection score (NDS) of 72.3 on the nuScenes validation set.

Student perspectives on designing and implementing an interprofessional education module in chronic disease management at primary care level in Vietnam

PurposeThis study explored students' perspectives on designing and implementing the new clinical interprofessional education (IPE) module for chronic disease management at the primary care level in the Vietnamese context.Design/methodology/approachStudents from seven different university-level healthcare programmes participated in a cross-sectional survey Course Experience Questionnaire-based study. Additionally, two open-ended questions were presented to gather qualitative data, mapping student perspectives. Statistical analyses and thematic analyses were performed.FindingsThe results show that students agree with quality statements about IPE design features, such as good teaching, clear goals and standards, appropriate workload, appropriate assessment and a blended learning approach. Answers to the open-ended comments pointed out the strengths of the IPE module in providing opportunities for interprofessional learning, improving interprofessional collaboration competencies, real-life interprofessional collaboration practice and continuous feedback from tutors. Students also reported weaknesses in the IPE module, including a lack of feedback on the care plan and logistics of the IPE module.Originality/valueStudents positively recognise key design features of the IPE module, combining classroom activities with standardised patient simulations, clinical practice and home visits. Chronic disease management at the primary care level is a suitable context for training students to work interprofessionally. The strengths and weaknesses identified by students could help in the redesign and future implementation of the IPE module in the Vietnamese context. They could inspire practices in Southeast Asian medical education and beyond.

Design Features of a Removable Module for Securing Containers in Gondola Cars

Purpose. The main purpose of the study is to highlight the design features of the design of a removable module for fastening containers in gondola cars. Methodology. For the safe transportation of containers in gondola cars, it is proposed to use a removable module. This module operates on the principle of an intermediate adapter between the container and the open wagon body. The choice of the profiles of the beams of the frame of the removable module was made according to the moments of resistance of their components. To do this, the core system of the removable module frame was constructed and calculated using the Lira CAD software package. According to the maximum bending moment acting in the module frame due to the moment of resistance and the known permissible stresses, the profile of the frame beams was selected. Taking into account the selected profile of the frame, its spatial model was built and strength calculations were performed. The finite element method implemented in SolidWorks Simulation was used for strength calculations. The calculation was carried out for two loading schemes of the removable module: the effect of a vertical load and the effect of vertical and longitudinal loads. Findings. Based on the calculation, the profile of the removable module was selected according to the determined value of the resistance moment - a square pipe with a height and width of 300 mm and a wall thickness of 5 mm. The results of the strength calculations of the removable module showed that for the case of its perception of a vertical load, the maximum stresses are 112.5 MPa. The maximum displacements occur in the upper parts of the superstructures and are about 1 mm. For the case of the vertical and longitudinal loads perceived by the removable module, the maximum stresses in its structure amounted to 287.6 MPa. The maximum displacements occurred in the end superstructure located on the side of the load applied to the removable module. These displacements amounted to 2.16 mm. Thus, the strength of the removable module for the considered loading schemes is ensured. Originality. A scientific approach to the design of a removable module for securing containers in gondola carsis proposed. Practical value. The research will contribute to the creation of recommendations for the design of modular-type vehicle structures, as well as to improving the efficiency of railway transport operation.

A Multi-Scale Temporal Feature Extraction Approach for Network Traffic Anomaly Detection

With the rapid advancement of network technology and the expanding scale of the Internet, network traffic data has grown exponentially, leading to increasingly prominent issues in network security. Many researchers are developing new network traffic anomaly detection models, but these models are difficult to effectively capture the multi-scale temporal characteristics of network traffic data and learn the correlation and importance among various feature dimensions. To this end, we propose a multi-scale temporal feature network (MSTFN-AM) based on an attention mechanism. MSTFN-AM integrates original data with temporal information through the temporal position encoding module and extracts multi-scale temporal features through the temporal feature extraction module. The temporal self-attention module can effectively identify dependencies and correlations between different time points. Ultimately, experiments conducted on two public datasets demonstrate that the MSTFN-AM model outperforms all baseline models in prediction performance.

SGNN-T: Space graph neural network coupled transformer for molecular property prediction

Molecular properties play a crucial role in material discovery, protein interaction and drug development. The appearance of Graph Neural Network (GNN) significantly improved the performance of molecular property prediction. However, nodes in GNN only update the features of neighbor nodes, resulting in insufficient ability to encode global feature information. The self- attention mechanism in transformer can encode the global information except for local information of molecules, while its spatial information is insufficient. Since molecules are three-dimensional spatial structures, spatial geometry information is an important attribute for molecules properties. To consider these factors, a network model of Space Graph Neural Network coupled Transformer (SGNN-T) is proposed in this paper which can combine global and local molecule information with three-dimensional spatial structures for molecular properties prediction. In this model, Graph neural network Geometric Feature Fusion Module (GGFF) and Transformer Spatial Geometric Feature Enhancement Module (TSGFE) are included to enhance the spatial geometry learning ability of the network. The GGFF module constructs a parallel graph neural network by thinking over atoms, bonds and bond angles at the same time which effectively complements the spatial information of the network by leading into bond angles than normal GNN. The TSGFE module introduces the coordinates and centrality degree features coupled with the features by GGFF into transformer to further enhance the geometric expression ability of the module. Through these two parts, SGNN-T model can encode local and global information of molecules at the same time. Property prediction experiments are executed on the QM9, OMDB and MEGNet dataset. The results of MAE show the proposed model has the best performance than the popular models.

A PV cell defect detector combined with transformer and attention mechanism

Automated defect detection in electroluminescence (EL) images of photovoltaic (PV) modules on production lines remains a significant challenge, crucial for replacing labor-intensive and costly manual inspections and enhancing production capacity. This paper presents a novel PV defect detection algorithm that leverages the YOLO architecture, integrating an attention mechanism and the Transformer module. We introduce a polarized self-attention mechanism in the feature extraction stage, enabling separate extraction of spatial and semantic features of PV modules, combined with the original input features, to enhance the network’s feature representation capabilities. Subsequently, we integrate the proposed CNN Combined Transformer (CCT) module into the model. The CCT module employs the transformer to extract contextual semantic information more effectively, improving detection accuracy. The experimental results demonstrate that the proposed method achieves a 77.9% mAP50 on the PVEL-AD dataset while preserving real-time detection capabilities. This method enhances the mAP50 by 17.2% compared to the baseline, and the mAP50:95 metric exhibits an 8.4% increase over the baseline.

A Method of Ship Target Modulation Feature Enhancement Based on Phase Information

Abstract Ship modulation features are commonly used in underwater acoustic target recognition, which contains propeller parameter information of the target. The basis processing of modulation spectrum is demodulation. Traditional envelope demodulation methods only consider amplitude information and make little use of phase information, which can fully characterize the dynamic characteristics of radiated noise. In this paper, a modulation feature enhancement method based on phase information is proposed, which takes full account of the phase information of the target signal. It uses the modified group delay function to demodulate the target signal, and utilizes the phase stability characteristics of the modulated line spectrum to enhance the modulation features of the ship target by weighted phase features. Simulation and sea trial data verification show that the proposed method can effectively enhance the energy of the line spectrum, suppress the background noise, and improve the signal-to-noise ratio of the modulation line spectrum, which lays a good foundation for further using the modulation line spectrum to carry out the estimation of modulation parameters, and has a good application prospect in the field of underwater acoustic target recognition.

ATAT: Astronomical Transformer for time series and Tabular data

Context. The advent of next-generation survey instruments, such as the Vera C. Rubin Observatory and its Legacy Survey of Space and Time (LSST), is opening a window for new research in time-domain astronomy. The Extended LSST Astronomical Time-Series Classification Challenge (ELAsTiCC) was created to test the capacity of brokers to deal with a simulated LSST stream. Aims. Our aim is to develop a next-generation model for the classification of variable astronomical objects. We describe ATAT, the Astronomical Transformer for time series And Tabular data, a classification model conceived by the ALeRCE alert broker to classify light curves from next-generation alert streams. ATAT was tested in production during the first round of the ELAsTiCC campaigns. Methods. ATAT consists of two transformer models that encode light curves and features using novel time modulation and quantile feature tokenizer mechanisms, respectively. ATAT was trained on different combinations of light curves, metadata, and features calculated over the light curves. We compare ATAT against the current ALeRCE classifier, a balanced hierarchical random forest (BHRF) trained on human-engineered features derived from light curves and metadata. Results. When trained on light curves and metadata, ATAT achieves a macro F1 score of 82.9 ± 0.4 in 20 classes, outperforming the BHRF model trained on 429 features, which achieves a macro F1 score of 79.4 ± 0.1. Conclusions. The use of transformer multimodal architectures, combining light curves and tabular data, opens new possibilities for classifying alerts from a new generation of large etendue telescopes, such as the Vera C. Rubin Observatory, in real-world brokering scenarios.

Real-time placental vessel segmentation in fetoscopic laser surgery for Twin-to-Twin Transfusion Syndrome

Twin-to-Twin Transfusion Syndrome (TTTS) is a rare condition that affects about 15% of monochorionic pregnancies, in which identical twins share a single placenta. Fetoscopic laser photocoagulation (FLP) is the standard treatment for TTTS, which significantly improves the survival of fetuses. The aim of FLP is to identify abnormal connections between blood vessels and to laser ablate them in order to equalize blood supply to both fetuses. However, performing fetoscopic surgery is challenging due to limited visibility, a narrow field of view, and significant variability among patients and domains. In order to enhance the visualization of placental vessels during surgery, we propose TTTSNet, a network architecture designed for real-time and accurate placental vessel segmentation. Our network architecture incorporates a novel channel attention module and multi-scale feature fusion module to precisely segment tiny placental vessels. To address the challenges posed by FLP-specific fiberscope and amniotic sac-based artifacts, we employed novel data augmentation techniques. These techniques simulate various artifacts, including laser pointer, amniotic sac particles, and structural and optical fiber artifacts. By incorporating these simulated artifacts during training, our network architecture demonstrated robust generalizability. We trained TTTSNet on a publicly available dataset of 2060 video frames from 18 independent fetoscopic procedures and evaluated it on a multi-center external dataset of 24 in-vivo procedures with a total of 2348 video frames. Our method achieved significant performance improvements compared to state-of-the-art methods, with a mean Intersection over Union of 78.26% for all placental vessels and 73.35% for a subset of tiny placental vessels. Moreover, our method achieved 172 and 152 frames per second on an A100 GPU, and Clara AGX, respectively. This potentially opens the door to real-time application during surgical procedures. The code is publicly available at https://github.com/SanoScience/TTTSNet.

Comprehensive analysis of polysulfone membrane humidifier in hydrogen fuel cell vehicles: Experimental and theoretical approaches

This study performed a comprehensive experimental and theoretical analysis of a shell-and-tube type gas-to-gas humidifier module in a 95 kW class hydrogen fuel cell vehicle. The humidifier module features a polysulfone hollow fiber membrane characterized by its hydrophilic and non-fluorinated properties. Experimental investigations were conducted to understand the impacts of the inlet temperature, relative humidity, and temperature differences on heat and mass transfer rates, and the approach dew point temperature (ADT). The results showed that the ADT increased from 12.42 to 15.89 °C with a temperature difference of 15 °C between the shell and tube inlet sides, as the flow rate increased from 1000 to 3400 L/min, with the highest performance observed at lower flow rates. Simultaneously, a detailed theoretical model was developed, incorporating a new dual-mode sorption approach that accounts for temperature dependency within the solution-diffusion mechanism across the membrane. The developed model was in good agreement with the experimental results, with a relative error of less than 5 %. Furthermore, the dynamic model accurately predicts the response of the humidifier module to step changes in feed flow rates, simulating load variations in the fuel cell vehicle. This provides valuable insights into the humidifier's adaptability and responsiveness, which are crucial for optimizing its performance under various operating conditions.

High-Precision Dichotomous Image Segmentation With Frequency and Scale Awareness.

Dichotomous image segmentation (DIS) with rich fine-grained details within a single image is a challenging task. Despite the plausible results achieved by deep learning-based methods, most of them fail to segment generic objects when the boundary is cluttered with the background. In fact, the gradual decrease in feature map resolution during the encoding stage and the misleading texture clue may be the main issues. To handle these issues, we devise a novel frequency-and scale-aware deep neural network (FSANet) for high-precision DIS. The core of our proposed FSANet is twofold. First, a multimodality fusion (MF) module that integrates the information in spatial and frequency domains is adopted to enhance the representation capability of image features. Second, a collaborative scale fusion module (CSFM) which deviates from the traditional serial structures is introduced to maintain high resolution during the entire feature encoding stage. In the decoder side, we introduce hierarchical context fusion (HCF) and selective feature fusion (SFF) modules to infer the segmentation results from the output features of the CSFM module. We conduct extensive experiments on several benchmark datasets and compare our proposed method with existing state-of-the-art (SOTA) methods. The experimental results demonstrate that our FSANet achieves superior performance both qualitatively and quantitatively. The code will be made available at https://github.com/chasecjg/FSANet.

Dual-drive RUL prediction of gear transmission systems based on dynamic model and unsupervised domain adaption under zero sample

Gear transmission systems are key components in rotating machinery, and its remaining useful life (RUL) prediction can provide adequate leading time for well-timed maintenance. Existing RUL prediction models usually rely on sufficient training data. However, the acquisition of full life cycle dataset of gear transmission system is difficult or impossible. To solve the problem on the difficult startup and poor generalization of prediction model under zero environment, a dual-drive prediction method based on dynamic model of gear transmission system and unsupervised domain adaptation is proposed in this paper. Firstly, a dynamic model of gear transmission system and growth mechanism of local defect is established to generate full-life cycle simulation data. Then, the multi-scale modulation features are extracted based on simulated and measured data. Furthermore, multi-scale temporal convolution operations are introduced into dual-channel unsupervised domain adaptation model. Besides, a compound principle of reverse truncation and forward expansion principle is investigated to determine the first prediction time. Finally, the validity of the proposed model is verified by two kinds of gearbox data. Ablation experiments are carried out to evaluate the contribution of each module in proposed model. In addition, the effectiveness and generalization ability of proposed model are verified when compared with other advanced transfer learning methods.

Evidence-based early rehabilitation for children with cerebral palsy: co-development of a multifaceted knowledge translation strategy for rehabilitation professionals.

Cerebral palsy (CP) is the most common childhood physical disability. Early and evidence-based rehabilitation is essential for improving functional outcomes in children with CP. However, rehabilitation professionals face barriers to adopting evidence-based practices (EBP)s. The objective of this project is to develop a knowledge translation (KT) strategy to support CP-EBP among pediatric rehabilitation professionals. We follow an integrated KT approach by collaborating with clinician- and patient-partners. Partners engaged in co-design through team meetings and content review via email. The KT strategy comprises two components: (1) An electronic (e)-KT toolkit was created from summarized evidence extracted from randomized clinical trials on early rehabilitation for children with CP, and (2) a multifaceted online KT training program developed with guidance from a scoping review exploring effective KT strategies. The e-KT toolkit summarizes twenty-two early interventions for children with or at risk for CP aged 0-5 years. Each module features an introduction, resources, parent/family section, and clinician information, including outcomes, intervention effectiveness, and evidence level. The KT training program includes three 10-15 min video-based training modules, text summaries, quizzes, and case studies. Site champions, identified as qualified rehabilitation professionals, were onboarded to support the site implementation of the training program. A champion-training booklet and 1-hour session were designed to equip them with the necessary knowledge/resources. The tailored, multifaceted, and co-designed KT strategy aims to be implemented in pediatric rehabilitation sites to support professional's uptake of CP-EBPs. Lessons learned from its development, including the co-development process and multifaceted nature, hold potential for broader applications in rehabilitation.