Channel Dimensions Research Articles

Hydrogen-Bonded Organic Framework and Metal-Organic Framework Assembly of Waterwheel PgC-Noria Molecule: Regulating Microstructure Enables Iodine Transfer.

Hydrogen-bonded organic frameworks (HOFs) are a type of crystalline porous materials self-assembled from organic or metal-organic building blocks via intermolecular hydrogen bonding, which have received increasing attention due to their reversible and flexible hydrogen bonding properties. Currently, it remains a challenge to construct HOFs based on complex or porous organic cages as molecular building blocks. Herein, a 3D HOF (PgC-HOF) featuring honeycomb-shaped channels is crafted utilizing a sizable waterwheel-like PgC-noria organic molecule cage. The pivotal role of intermolecular multipoint hydrogen bonding interactions in upholding structural integrity and stability is underscored by the possession of 36 phenolic hydroxyl groups in PgC-HOF. Interestingly, the introduction of calcium ions into the reaction system results in the formation of the metal-organic framework (PgC-MOF), with the channel dimensions increasing from 6.8 to 9.1Å. Furthermore, I2 sorption/release experiments are conducted on PgC-HOF and PgC-MOF, achieving an increase in the optimal adsorption amount from 1.45 to 2.19gg-1 and a transition from an irreversible adsorbent to a reversible adsorbent.

DEUFormer: High‐precision semantic segmentation for urban remote sensing images

AbstractUrban remote sensing image semantic segmentation has a wide range of applications, such as urban planning, resource exploration, intelligent transportation, and other scenarios. Although UNetFormer performs well by introducing the self‐attention mechanism of Transformer, it still faces challenges arising from relatively low segmentation accuracy and significant edge segmentation errors. To this end, this paper proposes DEUFormer by employing a special weighted sum method to fuse the features of the encoder and the decoder, thus capturing both local details and global context information. Moreover, an Enhanced Feature Refinement Head is designed to finely re‐weight features on the channel dimension and narrow the semantic gap between shallow and deep features, thereby enhancing multi‐scale feature extraction. Additionally, an Edge‐Guided Context Module is introduced to enhance edge areas through effective edge detection, which can improve edge information extraction. Experimental results show that DEUFormer achieves an average Mean Intersection over Union (mIoU) of 53.8% on the LoveDA dataset and 69.1% on the UAVid dataset. Notably, the mIoU of buildings in the LoveDA dataset is 5.0% higher than that of UNetFormer. The proposed model outperforms methods such as UNetFormer on multiple datasets, which demonstrates its effectiveness.

Lightweight defect detection algorithm of tunnel lining based on knowledge distillation

Due to the influence of construction quality, engineering geology and hydrological environment, defects such as dehollowing and insufficient compaction can occur in tunnels. Aiming at the problems of complex detection model, poor real-time performance and low accuracy of the current tunnel lining defect detection methods, the study proposes a lightweight defect detection algorithm of tunnel lining based on knowledge distillation. Firstly, a high-precision teacher model based on yolov5s was constructed by constructing a C3CSFM module that combines residual structure and attention mechanism, a MDFPN network structure with multi-scale feature fusion and a reweighted RWNMS re-screening mechanism. Secondly, in the distillation process, the feature and output dimension results are fused to improve the detection accuracy, and the mask feature relationship is learned in the space and channel dimension to improve the real-time detection. Tests on the tunnel lining radar defect image dataset showed that the number of parameters of the improved model was reduced from 16.03 MB to 3.20 MB, a reduction of 80%, and the average accuracy was improved from 83.4 to 86.5%, an increase of 3.1%. On the basis of maintaining the structure and detection performance of the model, the lightweight degree of the model is greatly improved, and the high-precision and real-time detection of tunnel lining defects is realized.

Revolutionizing automated pear picking using Mamba architecture

With the emergence of the new generation vision architecture Vmamba and the further demand for agricultural yield and efficiency, we propose an efficient and high-accuracy target detection network for automated pear picking tasks based on Vmamba, aiming to address the issue of low efficiency in current Transformer architectures. The proposed network, named SRSMamba, employs a Reward and Punishment Mechanism (RPM) to focus on important information while minimizing redundancy interference. It utilizes 3D Selective Scan (SS3D) to extend scanning dimensions and integrates global information across channel dimensions, thereby enhancing the model's robustness in complex agricultural environments and effectively adapting to the extraction of complex features in pear orchards and farmlands. Additionally, a Stacked Feature Pyramid Network (SFPN) is introduced to enhance semantic information during the feature fusion stage, particularly improving the detection capability for small targets. Experimental results show that SRSMamba has a low parameter count of 21.1 M, GFLOPs of 50.4, mAP of 72.0%, mAP50 reaching 94.8%, mAP75 at 68.1%, and FPS at 26.9. Compared with other state-of-the-art (SOTA) object detection methods, it achieves a good trade-off between model efficiency and detection accuracy.

Ultra-Stretchable Microfluidic Devices for Optimizing Particle Manipulation in Viscoelastic Fluids.

Viscoelastic microfluidics leverages the unique properties of non-Newtonian fluids to manipulate and separate micro- or submicron particles. Channel geometry and dimension are crucial for device performance. Traditional rigid microfluidic devices require numerous iterations of fabrication and testing to optimize these parameters, which is time-consuming and costly. In this work, we developed a flexible microfluidic device using ultra-stretchable and biocompatible Flexdym material to overcome this issue. Our device allows for simultaneous modification of channel dimensions by external stretching. We fabricated a stretchable device with an initial square microchannel (30 μm × 30 μm), and the channel aspect ratio can be adjusted from 1 to 5 by external stretching. Next, the effects of aspect ratio, particle size, flow rate, and poly(ethylene oxide) (PEO) concentration that make the fluid viscoelastic on particle migration were investigated. Finally, we demonstrated the feasibility of our approach by testing channels with an aspect ratio of 3 for the separation of both particles and cells.

Adaptation of Object Detection Algorithms for Road Indicator Lights in Complex Scenes

In the realm of autonomous driving, practical driving scenarios are fraught with numerous complexities, including inclement weather conditions, nighttime blurriness, and ambient light sources that significantly hinder drivers’ ability to discern road indicators. Furthermore, the dynamic nature of road indicators, which are constantly evolving, poses additional challenges for computer vision-based detection systems. To address these issues, this paper introduces a road indicator light detection model, leveraging the advanced capabilities of YOLOv8. We have ingeniously integrated the robust backbone of YOLOv8 with four distinct attention mechanism modules—Convolutional Block Attention Module (CBAM), Efficient Channel Attention (ECA), Shuffle Attention (SA), and Global Attention Mechanism (GAM)—to significantly enhance the model performance in capturing nuanced features of road indicators and boosting the accuracy of detecting minute objects. Additionally, we employ the Asymptotic Feature Pyramid Network (AFPN) strategy, which optimizes the fusion of features across different scales, ensuring not only an enhanced performance but also maintaining real-time capability. These innovative attention modules empower the model by recalibrating the significance of both channel and spatial dimensions within the feature maps, enabling it to hone in on the most pertinent object characteristics. To tackle the challenges posed by samples rich in small, occluded, background-similar objects, and those that are inherently difficult to recognize, we have incorporated the Focaler-IOU loss function. This loss function deftly reduces the contribution of easily detectable samples to the overall loss, thereby intensifying the focus on challenging samples. This strategic balancing of hard-to-detect versus easy-to-detect samples effectively elevates the model’s detection performance. Experimental evaluations conducted on both a public traffic signal dataset and a proprietary headlight dataset have yielded impressive results, with both mAP50 and mAP50:95 metrics experiencing significant improvements exceeding two percentage points. Notably, the enhancements observed in the headlight dataset are particularly profound, signifying a significant step forward toward realizing safer and more reliable assisted driving technologies.

Dual-path aggregation transformer network for super-resolution with images occlusions and variability

While Transformer-based approaches have recently achieved notable success in super-resolution, their extensive computational requirements impede widespread practical adoption. High-resolution meteorological satellite cloud imagery is essential for weather analysis and forecasting. Enhancing image resolution through super-resolution techniques facilitates the accurate identification and localization of geographic features by meteorological systems. However, current super-resolution methods fail to restore the intricacies of cloud formations and complex regions fully. This research introduces a novel dual-path aggregation Transformer network (DPAT) tailored to enhance the super-resolution of meteorological satellite cloud images. The DPAT network adeptly captures cloud imagery's subtle details and textures, effectively addressing occlusions and the variability inherent in satellite imagery. It bolsters the model's ability to manage the complex attributes of cloud images through the introduction of the Dual-path Aggregation Self-Attention (DASA) mechanism and the Multi-scale Feature Aggregation Block (MFAB), thereby enhancing performance in processing intricate cloud features. The DASA mechanism synthesizes features across spatial, depth, and channel dimensions via a dual-path approach, thoroughly exploiting feature correlations. The MFAB, designed to supplant the multilayer perceptron, incorporates shift convolution and a multi-scale interaction block to augment feature information, compensating for the deficiency in local information absorption due to fixed receptive fields. Experimental outcomes indicate that DPAT delivers superior super-resolution outcomes. With a parameter count of only 32% of the Enhanced Deep Residual Network (EDSR) or 77% of the Image Restoration using Shift Window Transformer (SwinIR), DPAT matches SwinIR's performance on the satellite cloud dataset. Moreover, DPAT balances accuracy and parameter economy across various datasets. This technology is expected to improve image super-resolution capabilities in multiple fields such as human action recognition and industrial recognition, and indirectly improve the accuracy of image perception tasks.

SGW-YOLOv8n: An Improved YOLOv8n-Based Model for Apple Detection and Segmentation in Complex Orchard Environments

This study addresses the problem of detecting occluded apples in complex unstructured environments in orchards and proposes an apple detection and segmentation model based on improved YOLOv8n-SGW-YOLOv8n. The model improves apple detection and segmentation by combining the SPD-Conv convolution module, the GAM global attention mechanism, and the Wise-IoU loss function, which enhances the accuracy and robustness. The SPD-Conv module preserves fine-grained features in the image by converting spatial information into channel information, which is particularly suitable for small target detection. The GAM global attention mechanism enhances the recognition of occluded targets by strengthening the feature representation of channel and spatial dimensions. The Wise-IoU loss function further optimises the regression accuracy of the target frame. Finally, the pre-prepared dataset is used for model training and validation. The results show that the SGW-YOLOv8n model significantly improves relative to the original YOLOv8n in target detection and instance segmentation tasks, especially in occlusion scenes. The model improves the detection mAP to 75.9% and the segmentation mAP to 75.7% and maintains a processing speed of 44.37 FPS, which can meet the real-time requirements, providing effective technical support for the detection and segmentation of fruits in complex unstructured environments for fruit harvesting robots.

Rib Fracture Detection Model Based on Faster-RCNN-SE-FA Algorithm

To address the problem of missed diagnosis in rib fracture detection from CT scans, this study introduces an enhanced model, called Faster-RCNN-SE-FA, which is built upon the traditional Faster-RCNN architecture. The proposed model integrates a novel filter anchor method and thoroughly considers the specific imaging characteristics of ribs in CT images. The preprocessing of the image is followed by applying the Squeeze-and-Excitation (SE) module, which enhances the discrimination of features in the channel dimension while preserving the location Sensitivity (Sen) important for target detection tasks. Consequently, this modification leads to a significant improvement in model performance. Empirical experiments, conducted on CT sequences of 130 rib feature cases provided by the First Affiliated Hospital of Ningbo University, demonstrate that the Faster-RCNN-SE-FA model achieves better Sen and accuracy compared to traditional methods, including the baseline Faster-RCNN.

Exploring the key drivers of inter-organizational knowledge transfer in projects: evidence from international construction projects

PurposeDespite the knowledge transfer in project contexts which has been extensively studied by scholars, the study on inter-organizational knowledge transfer in international construction projects is still scattered and unsystematic. This research aims to explore the key factors influencing inter-organizational knowledge transfer of international construction projects and analyze how these factors interact to create a synthetic framework that enhances the effectiveness of knowledge transfer (EKT).Design/methodology/approachAt first, eight factors influencing inter-organizational knowledge transfer within international construction projects were identified, which were from the four dimensions of subject, relationship, channel and context, namely cultural distance, connection strength, organizational climate, intercultural competence, information technology capability, transmit willingness, receive willingness and richness of transfer channels. Then, a conceptual model was developed and 13 hypotheses were formulated, which were derived from a literature review and in-depth survey. After that, data from 353 respondents were collected and analyzed, and the hypotheses were tested by structural equation modeling analysis and bootstrapping test.FindingsThe results suggest that cultural distance hinders transfer willingness, which further affects EKT. Connection strength affects EKT by positively influencing transfer willingness and channel. Besides, organizational climate and intercultural competence positively influence transfer willingness and further affect EKT, while information technology capability affects the richness of transfer channels.Originality/valueThis research gives a thorough examination of the determinants influencing inter-organizational knowledge transfer of international construction projects, thus formulating available approaches that project managers and personnel can employ to effectively facilitate EKT.

Feature fusion and context interaction for RGB-D indoor semantic segmentation

Depth information in indoor scenes is crucial to understanding the spatial and occluding relationships of objects. Depth information contains rich geometric details and is unaffected by lighting, texture, and color, significantly enhancing traditional color images. RGB images and depth images have been widely applied in various image analysis. However, effectively leveraging the complementary information from both modalities remains a challenge. To address this issue, we propose a unified and effective feature fusion and context interaction network (FCINet). It includes three modules. Firstly, we construct a multi-modal feature fusion module (MFFM), which can achieve the aggregation of two modalities along spatial and channel dimensions. Secondly, we construct a global and local information interaction context module (GLCM) to encode rich semantic information. Finally, we construct a feature alignment and fusion module (FAFM), which integrates upsampled low-level features with high-level features to mitigate spatial information loss. Experimental results indicate that the model achieved favorable outcomes on both the NYU Depth v2 dataset and the more complex SUN RGB-D dataset.

DeepZoning: Re-accelerate CNN Inference with Zoning Graph for Heterogeneous Edge Cluster

Parallelizing CNN inference on heterogeneous edge clusters with data parallelism has gained popularity as a way to meet real-time requirements without sacrificing model accuracy. However, existing algorithms struggle to find optimal parallel granularity for complex CNNS, the structure of which is a directed acyclic graph (DAG) rather than a chain, and the parallel dimension is inflexible. To distribute the workload of modern CNNs on heterogeneous devices is also proven as NP-hard problem. In this paper, we introduce DeepZoning , a versatile and cooperative inference framework that combines both model and data parallelism to accelerate CNN inference. DeepZoning employs two algorithms at different levels: (1) a low-level Adaptive Workload Partition algorithm that uses linear programming and takes spatial and channel dimensions into optimization during the search for feature map distribution on heterogeneous devices, and (2) a high-level Model Partition algorithm that finds the optimal model granularity and organizes complex CNNs into sequential zones to balance communication and computation during execution. Our experimental evaluations show that DeepZoning is effective, achieving up to a 3.02 × speed improvement on our experimental prototype compared to state-of-the-art algorithms.

Dual-attention transformer-based hybrid network for multi-modal medical image segmentation

Accurate medical image segmentation plays a vital role in clinical practice. Convolutional Neural Network and Transformer are mainstream architectures for this task. However, convolutional neural network lacks the ability of modeling global dependency while Transformer cannot extract local details. In this paper, we propose DATTNet, DualATTentionNetwork, an encoder-decoder deep learning model for medical image segmentation. DATTNet is exploited in hierarchical fashion with two novel components: (1) Dual Attention module is designed to model global dependency in spatial and channel dimensions. (2) Context Fusion Bridge is presented to remix the feature maps with multiple scales and construct their correlations. The experiments on ACDC, Synapse and Kvasir-SEG datasets are conducted to evaluate the performance of DATTNet. Our proposed model shows superior performance, effectiveness and robustness compared to SOTA methods, with mean Dice Similarity Coefficient scores of 92.2%, 84.5% and 89.1% on cardiac, abdominal organs and gastrointestinal poly segmentation tasks. The quantitative and qualitative results demonstrate that our proposed DATTNet attains favorable capability across different modalities (MRI, CT, and endoscopy) and can be generalized to various tasks. Therefore, it is envisaged as being potential for practicable clinical applications. The code has been released on https://github.com/MhZhang123/DATTNet/tree/main.

DCMR: Degradation compensation and multi-dimensional reconstruction based pre-processing for video coding

The rapid growth of video data poses a serious challenge to the limited bandwidth. Video coding pre-processing technology can remove coding noise without changing the architecture of the codec. Therefore, it can improve the coding efficiency while ensuring a high degree of compatibility with existing codec. However, the existing pre-processing methods have the problem of feature redundancy, and lack an effective mechanism to recover high-frequency details. In view of these problems, we propose a Degradation Compensation and Multi-dimensional Reconstruction (DCMR) pre-processing method for video coding to improve compression efficiency. Firstly, we develop a degradation compensation model, which aims at filtering the coding noise in the original video and relieving the frame quality degradation caused by transmission. Secondly, we construct a lightweight multi-dimensional feature reconstruction network, which combines residual learning and feature distillation. It aims to enhance and refine the key features related to coding from both spatial and channel dimensions while suppressing irrelevant features. In addition, we design a weighted guided image filter detail enhancement convolution module, which is specifically used to recover the high-frequency details lost in the denoising process. Finally, we introduce an adaptive discrete cosine transform loss to balance coding efficiency and quality. Experimental results demonstrate that compared with the original codec H.266/VVC, the proposed DCMR can achieve BD-rate (VMAF) and BD-rate (MOS) gains by 21.62% and 12.99% respectively on VVC, UVG, and MCL-JCV datasets.

Single-view 3D reconstruction via dual attention

Constructing global context information and local fine-grained information simultaneously is extremely important for single-view 3D reconstruction. In this study, we propose a network that uses spatial dimension attention and channel dimension attention for single-view 3D reconstruction, named R3Davit. Specifically, R3Davit consists of an encoder and a decoder, where the encoder comes from the Davit backbone network. Different from the previous transformer backbone network, Davit focuses on spatial and channel dimensions, fully constructing global context information and local fine-grained information while maintaining linear complexity. To effectively learn features from dual attention and maintain the overall inference speed of the network, we do not use a self-attention layer in the decoder but design a decoder with a nonlinear reinforcement block, a selective state space model block, and an up-sampling Residual Block. The nonlinear enhancement block is used to enhance the nonlinear learning ability of the network. The Selective State Space Model Block replaces the role of the self-attention layer and maintains linear complexity. The up-sampling Residual Block converts voxel features into a voxel model while retaining the voxels of this layer. Features are used in the up-sampling block of the next layer. Experiments on the synthetic dataset ShapeNet and ShapeNetChairRFC with random background show that our method outperforms recent state of the art (SOTA) methods, we lead by 1% and 2% in IOU and F1 scores, respectively. Simultaneously, experiments on the real-world dataset Pix3d fully prove the robustness of our method. The code will be available at https://github.com/epicgzs1112/R3Davit.

Enhanced Acoustic Mixing in Silicon-Based Chips with Sharp-Edged Micro-Structures

The small dimensions of microfluidic channels allow for fast diffusive or passive mixing, which is beneficial for time-sensitive applications such as chemical reactions, biological assays, and the transport of to-be-detected species to sensors. In microfluidics, the need for fast mixing within milliseconds arises primarily because these devices are often used in fields where rapid and efficient mixing significantly impacts the performance and outcome of the processes. Active mixing with acoustics in microfluidic devices involves using acoustic waves to enhance the mixing of fluids within microchannels. Using sharp corners and wall patterns in acoustofluidic devices significantly enhances the mixing by acoustic streaming around these features. The streaming patterns around the sharp edges are particularly effective for the mixing because they can produce strong lateral flows that rapidly homogenize liquids. This work presents extensive characterizations of the effect of sharp-edged structures on acoustic mixing in bulk acoustic wave (BAW) mode in a silicon microdevice. The effect of side wall patterns in different angles and shapes, their positions, the type of piezoelectric transducer, and its amplitude and frequency have been studied. Following the patterning of the channel walls, a mixing time of 25 times faster was reached, compared to channels with smooth side walls exhibiting conventional BAW behavior. The average locally determined acoustic streaming velocity inside the channel becomes 14 times faster if sharp corners of 10° are added to the wall.

CosGCTFormer: An end-to-end driver state recognition framework

Driver state is critical for safety in mixed traffic environments with automated and human-driven vehicles. Recognizing and monitoring the driver’s state is essential to provide early warnings of abnormal driving performance. This paper proposes a driver state recognition framework called cosGCTFormer for end-to-end recognition of the driver’s emotional state and mental load. First, we employ an inverted residual convolution module to extract local features from physiological signal data. Second, the cosGCT module is introduced to extract global features. This module uses cos attention to address the high computational complexity of the Transformer in calculating attention weights. Additionally, it employs Gated Channel Transformation (GCT) to establish relationships between different channel dimensions, something the Transformer alone cannot do effectively. Finally, a simulated driving experiment was designed to collect data for training and testing the proposed method. Results demonstrate that our framework achieves higher accuracy and a more lightweight design compared to prevalent Convolution neural network-based (CNN-based) and Transformer-based methods. Additionally, the effectiveness of both modules was verified.

Combine multi-order representation learning and frame optimization learning for skeleton-based action recognition

Skeleton-based action recognition has broad application prospects in many fields such as virtual reality. Currently, the most popular way is to employ Graph Convolutional Networks (GCNs) or Hypergraph Convolutional Networks (HGCNs) for this task. However, GCN-based methods may heavily rely on the physical connectivity relationship between joints while lack the capture of higher-order information about interactions among distant joints, and HGCN-based methods usually introduce unnecessary noise when capturing low-order information of skeleton structures with simple topology. Besides, the current methods do not deal well with redundant frames and confusing frames. These limitations hinder the improvement of recognition accuracy. In this paper, we propose a novel network, called Hyper-Net, which combines multi-order representation learning and frame optimization learning for skeleton-based action recognition. Specifically, the proposed Hyper-Net contains Temporal-Channel Aggregation Graph Convolution (TCA-GC), Spatial-Temporal Aggregation Hypergraph Convolution (STA-HC) and Frame Optimization Learning (F-OL) modules. The TCA-GC aggregates low-order and local information from simple joint and bone topologies across different temporal and channel dimensions. The STA-HC captures high-order and global information from complex motion streams as well as solving the problem of spatial-temporal weight imbalance. The F-OL can adaptively extract key frames and distinguish confusing frames, thus improving the ability of the network to recognize confusing actions. A large number of experiments are conducted on the NTU RGB+D, NTU RGB+D 120 and NW-UCLA datasets for action recognition task. Experimental results demonstrate the superiority and effectiveness of the proposed network.

Research on fault diagnosis of mechanical component in high-speed train based on one-dimensional convolutional block residual channel attention

The yaw damper and the rolling bearing are key components to ensure the safe and reliable operation of high-speed trains. However, fault identification in these components remains challenging, with traditional feature extraction methods often suffering from low diagnostic accuracy. To address these issues, a one-dimensional convolutional block residual channel attention (1DCBRCA) method for fault diagnosis of high-speed train components is introduced. Firstly, the convolutional layer is constructed for feature extraction, and the convolution block attention module is utilized for adaptive feature optimization in both channel and space dimensions. Subsequently, a residual neural network model is established, with channel attention added after each residual block to focus on critical feature information, which is used for adjusting the weight parameters. Furthermore, the proposed method was validated through damper fault experiments and bearing fault experiments. Comparative analysis with state-of-the-art methods demonstrated that the proposed 1DCBRCA approach achieves higher diagnostic accuracy, confirming its potential to enhance fault diagnosis in high-speed train components.

Axial capacity of cold-formed steel channel sections with slits

Cold-formed steel (CFS) studs often include slits in their webs to improve thermal performance. However, the inclusion of such slits reduces their axial capacity. The literature contains limited experimental studies on the axial capacity of these studs with slits, and no non-linear elasto-plastic finite element studies have been reported. This research aims to address this gap. Non-linear elasto-plastic finite element models were developed and validated against experimental test results. A parametric study consisting of 960 finite element analysis (FEA) models was then conducted, covering the effects of different channel dimensions, slit sizes, section thicknesses, and stud lengths. Ultimate axial capacities obtained from these analyses were compared to the current codified direct strength method (DSM) predictions as per the Australian/New Zealand Standard AS/NZS 4600. Finally, design recommendations in the form of strength reduction factors are proposed. Based on these recommendations, modified DSM design equations with a reliability index above 2.5 are presented.