DSFNet: Convolutional Encoder-Decoder Architecture Combined Dual-GCN and Stand-alone Self-attention by Fast Normalized Fusion for Polyps Segmentation

In the past few decades, deep learning technology has been widely used in medical image segmentation and has made significant breakthroughs in the fields of liver and liver tumor segmentation, brain and brain tumor segmentation, video disc segmentation, heart image segmentation, and so on. However, the segmentation of polyps is still a challenging task since the surface of the polyps is flat and the color is very similar to that of surrounding tissues. Thus, It leads to the problems of the unclear boundary between polyps and surrounding mucosa, local overexposure, and bright spot reflection. To counter this problem, this paper presents a novel U-shaped network, namely DSFNet, which effectively combines the advantages of Dual-GCN and self-attention mechanisms. First, we introduce a feature enhancement block module based on Dual-GCN module as an attention mechanism to enhance the feature extraction of local spatial and structural information with fine granularity. Second, the stand-alone self-attention module is designed to enhance the integration ability of the decoding stage model to global information. Finally, the Fast Normalized Fusion method with trainable weights is used to efficiently fuse the corresponding three feature graphs in encoding, bottleneck, and decoding blocks, thus promoting information transmission and reducing the semantic gap between encoder and decoder. Our model is tested on two public datasets including Endoscene and Kvasir-SEG and compared with other state-of-the-art models. Experimental results show that the proposed model surpasses other competitors in many indicators, such as Dice, MAE, and IoU. In the meantime, ablation studies are also conducted to verify the efficacy and effectiveness of each module. Qualitative and quantitative analysis indicates that the proposed model has great clinical significance.Comment: 10 pages, 6 figures, 3 table

General Debiasing for Multimodal Sentiment Analysis

Existing work on Multimodal Sentiment Analysis (MSA) utilizes multimodal information for prediction yet unavoidably suffers from fitting the spurious correlations between multimodal features and sentiment labels. For example, if most videos with a blue background have positive labels in a dataset, the model will rely on such correlations for prediction, while ``blue background'' is not a sentiment-related feature. To address this problem, we define a general debiasing MSA task, which aims to enhance the Out-Of-Distribution (OOD) generalization ability of MSA models by reducing their reliance on spurious correlations. To this end, we propose a general debiasing framework based on Inverse Probability Weighting (IPW), which adaptively assigns small weights to the samples with larger bias i.e., the severer spurious correlations). The key to this debiasing framework is to estimate the bias of each sample, which is achieved by two steps: 1) disentangling the robust features and biased features in each modality, and 2) utilizing the biased features to estimate the bias. Finally, we employ IPW to reduce the effects of large-biased samples, facilitating robust feature learning for sentiment prediction. To examine the model's generalization ability, we keep the original testing sets on two benchmarks and additionally construct multiple unimodal and multimodal OOD testing sets. The empirical results demonstrate the superior generalization ability of our proposed framework. We have released the code and data to facilitate the reproduction

Effect of Protamex Hydrolysis on Foaming Properties and Structural Properties of Corn Glutelin

The effects of different durations of hydrolysis with Protemex on the foaming properties, surface tension, physicochemical properties and static rheological properties of corn glutelin were determined. The results showed that the solubility and foaming properties of corn glutelin were significantly improved by Protamex hydrolysis. The foaming capacity of the 120 min hydrolysate was highest, which was 2.8 times higher than that of corn glutelin, and its foam stability was also good. The hydrolysate had the lowest surface tension and the highest apparent viscosity. The microscopic morphology of the foam formed was fine and uniform, with a thick protein film. With the prolongation of hydrolysis time, the average particle size of corn glutelin hydrolysates decreased continuously, the endogenous fluorescence intensity and surface hydrophobicity increased gradually, while the surface net charge decreased first and then increased. The results of Raman spectroscopy showed that after appropriate hydrolysis, the α-helix content decreased, and the random coil and β-angle contents increased; the peak intensity ratio of tyrosine residues (I850/I830) increased, and the peak intensity of tryptophan residues (I760) decreased. Nevertheless, the β-folding content changed little. Long-time hydrolysis significantly increased the content of random coil and decreased the peak intensity ratio of tyrosine residues (I850/I830). Therefore, restricted hydrolysis can change the structure and interface properties of corn glutelin, improve its foam properties, and consequently increase the potential utilization rate of corn gluten meal in the food field

A nonlinear triboelectric nanogenerator with a broadened bandwidth for effective harvesting of vibration energy

A narrow resonance bandwidth of an energy harvesters limits its response to the wide frequency spectrum in ambient environments. This work proposes an addition of a nonlinear restoring force applied to a triboelectric nanogenerator (TENG) to tune and broaden the resonance bandwidth. This restoring force is applied by permanent magnets at both sides of the slider and two external magnets. The noncontact strategy is adopted between the slider and the grating electrodes to avoid the wear of electrodes and energy loss caused by friction. The results show that compared with the linear system, the nonlinear noncontact TENG (NN-TENG) can increase the peak current from 6.3 μA to 7.89 μA, with an increment of about 25%, increase the peak power from 650 μW to 977 μW, increasing by about 50%, and increase the bandwidth from 0.5 Hz to 7.75 Hz, increasing by about1400%. This work may enable a new strategy to boost the bandwidth and output power of TENG through nonlinear oscillators

Compliant and stretchable thermoelectric coils for energy harvesting in miniature flexible devices

With accelerating trends in miniaturization of semiconductor devices, techniques for energy harvesting become increasingly important, especially in wearable technologies and sensors for the internet of things. Although thermoelectric systems have many attractive attributes in this context, maintaining large temperature differences across the device terminals and achieving low–thermal impedance interfaces to the surrounding environment become increasingly difficult to achieve as the characteristic dimensions decrease. Here, we propose and demonstrate an architectural solution to this problem, where thin-film active materials integrate into compliant, open three-dimensional (3D) forms. This approach not only enables efficient thermal impedance matching but also multiplies the heat flow through the harvester, thereby increasing the efficiencies for power conversion. Interconnected arrays of 3D thermoelectric coils built using microscale ribbons of monocrystalline silicon as the active material demonstrate these concepts. Quantitative measurements and simulations establish the basic operating principles and the key design features. The results suggest a scalable strategy for deploying hard thermoelectric thin-film materials in harvesters that can integrate effectively with soft materials systems, including those of the human body

Large-Scale Measurement Layout Optimization Method Based on Laser Multilateration

Laser multilateration is a measurement method based on the distance intersection of multiple laser trackers which has been widely used in large-scale measurements. However, the layout of laser trackers has a great impact on the final measurement accuracy. In order to improve the overall measurement accuracy, firstly, a measurement uncertainty model based on laser multilateration is established. Secondly, a fast laser intersection detection constraint algorithm based on a k-DOPS bounding box and an adaptive target ball incident angle constraint detection algorithm are established for large-scale measurement scenes. Finally, the constrained layout optimization of the laser trackers is realized by using an improved cellular genetic algorithm. The results show that the optimized system layout can achieve the full coverage of measurement points and has higher measurement accuracy. Compared with the traditional genetic algorithm, the improved cellular genetic algorithm converges faster and obtains a better position layout

Large-Scale Measurement Layout Optimization Method Based on Laser Multilateration

Laser multilateration is a measurement method based on the distance intersection of multiple laser trackers which has been widely used in large-scale measurements. However, the layout of laser trackers has a great impact on the final measurement accuracy. In order to improve the overall measurement accuracy, firstly, a measurement uncertainty model based on laser multilateration is established. Secondly, a fast laser intersection detection constraint algorithm based on a k-DOPS bounding box and an adaptive target ball incident angle constraint detection algorithm are established for large-scale measurement scenes. Finally, the constrained layout optimization of the laser trackers is realized by using an improved cellular genetic algorithm. The results show that the optimized system layout can achieve the full coverage of measurement points and has higher measurement accuracy. Compared with the traditional genetic algorithm, the improved cellular genetic algorithm converges faster and obtains a better position layout