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The segmentation of abnormal regions is vital in smart manufacturing. The blurring sauce-packet leakage segmentation task (BSLST) is designed to distinguish the sauce packet and the leakage's foreground and background at the pixel level. However, the existing segmentation system for detecting sauce-packet leakage on intelligent sensors encounters an issue of imaging blurring caused by uneven illumination. This issue adversely affects segmentation performance, thereby hindering the measurements of leakage area and impeding the automated sauce-packet production. To alleviate this issue, we propose the two-stage illumination-aware sauce-packet leakage segmentation (ISLS) method for intelligent sensors. The ISLS comprises two main stages: illumination-aware region enhancement and leakage region segmentation. In the first stage, YOLO-Fastestv2 is employed to capture the Region of Interest (ROI), which reduces redundancy computations. Additionally, we propose image enhancement to relieve the impact of uneven illumination, enhancing the texture details of the ROI. In the second stage, we propose a novel feature extraction network. Specifically, we propose the multi-scale feature fusion module (MFFM) and the Sequential Self-Attention Mechanism (SSAM) to capture discriminative representations of leakage. The multi-level features are fused by the MFFM with a small number of parameters, which capture leakage semantics at different scales. The SSAM realizes the enhancement of valid features and the suppression of invalid features by the adaptive weighting of spatial and channel dimensions. Furthermore, we generate a self-built dataset of sauce packets, including 606 images with various leakage areas. Comprehensive experiments demonstrate that our ISLS method shows better results than several state-of-the-art methods, with additional performance analyses deployed on intelligent sensors to affirm the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

Accurately segmenting the optic disk (OD) and optic cup (OC) on retinal fundus images is important for treating glaucoma. With the development of deep learning, some CNN-based methods have been implemented to segment OD and OC, but it is difficult to accurately segment OD and OC boundaries affected by blood vessels and the lesion area. To this end, we propose a novel boundary-enhanced adaptive context network (BEAC-Net) for OD and OC segmentation. Firstly, a newly designed efficient boundary pixel attention (EBPA) module enhances pixel-by-pixel feature capture to collect the boundary contextual information of OD and OC in the horizontal and vertical directions. In addition, background noise makes segmenting boundary pixels difficult. To this end, an adaptive context module (ACM) was designed, which simultaneously learns local-range and long-range information to capture richer context. Finally, BEAC-Net adaptively integrates the feature maps from different levels using the attentional feature fusion (AFF) module. In addition, we provide a high-quality retinal fundus image dataset named the 66 Vision-Tech dataset, which advances the field of diagnostic glaucoma. Our proposed BEAC-Net was used to perform extensive experiments on the RIM-ONE-v3, DRISHTI-GS, and 66 Vision-Tech datasets. In particular, BEAC-Net achieved a Dice coefficient of 0.8267 and an IoU of 0.8138 for OD segmentation and a Dice coefficient of 0.8057 and an IoU value of 0.7858 for OC segmentation on the 66 Vision-Tech dataset, achieving state-of-the-art segmentation results. [ABSTRACT FROM AUTHOR]

The formulation in which biochemical enzymes are administered in polymer science plays a key role in retaining their catalytic activity. The one-step synthesis of polymers with highly sequence-controlled enzymes is a strategy employed to provide enzymes with higher catalytic activity and thermostability in material sustainability. Enzyme-catalyzed chain growth polymerization reactions using activated monomers, protein–polymer complexation techniques, covalent and non-covalent interaction, and electrostatic interactions can provide means to develop formulations that maintain the stability of the enzyme during complex material processes. Multifarious applications of catalytic enzymes are usually attributed to their efficiency, pH, and temperature, thus, progressing with a critical structure-controlled synthesis of polymer materials. Due to the obvious economics of manufacturing and environmental sustainability, the green synthesis of enzyme-catalyzed materials has attracted significant interest. Several enzymes from microorganisms and plants via enzyme-mediated material synthesis have provided a viable alternative for the appropriate synthesis of polymers, effectively utilizing the one-step approach. This review analyzes more and deeper strategies and material technologies widely used in multi-enzyme cascade platforms for engineering polymer materials, as well as their potential industrial applications, to provide an update on current trends and gaps in the one-step synthesis of materials using catalytic enzymes. [ABSTRACT FROM AUTHOR]

The thermal imaging pedestrian-detection system has excellent performance in different lighting scenarios, but there are problems regarding weak texture, object occlusion, and small objects. Meanwhile, large high-performance models have higher latency on edge devices with limited computing power. To solve the above problems, in this paper, we propose a real-time thermal imaging pedestrian-detection method for edge computing devices. Firstly, we utilize multi-scale mosaic data augmentation to enhance the diversity and texture of objects, which alleviates the impact of complex environments. Then, the parameter-free attention mechanism is introduced into the network to enhance features, which barely increases the computing cost of the network. Finally, we accelerate multi-channel video detection through quantization and multi-threading techniques on edge computing devices. Additionally, we create a high-quality thermal infrared dataset to facilitate the research. The comparative experiments on the self-built dataset, YDTIP, and three public datasets, with other methods show that our method also has certain advantages. [ABSTRACT FROM AUTHOR]

In this study we investigated the antimicrobial activity of magnolol on Staphylococcus aureus. The minimal inhibitory concentrations of magnolol against 31 S. aureus strains ranged from 4-32 μg/mL. In addition, hemolysin assays, Western blotting, and real-time RT-PCR were performed to investigate the effect of magnolol on α-toxin secretion by both methicillin-sensitive S. aureus (MSSA) and methicillin-resistant S. aureus (MRSA). The results indicated that sub-inhibitory concentrations of magnolol dosedependently inhibited the transcription of hla (the gene encoding α-toxin) in S. aureus, resulting in a reduction of α-toxin secretion and, thus, hemolytic activities. [ABSTRACT FROM AUTHOR]

Due to changes in illumination, adverse weather conditions, and interference from signs similar to real traffic signs, the false detection of traffic signs is possible. Nevertheless, in order to improve the detection effect of small targets, baseline SSD (single shot multibox detector) adopts a multi-scale feature detection method to improve the detection effect to some extent. The detection effect of small targets is improved, but the number of calculations needed for the baseline SSD network is large. To this end, we propose a lightweight SSD network algorithm. This method uses some 1 × 1 convolution kernels to replace some of the 3 × 3 convolution kernels in the baseline network and deletes some convolutional layers to reduce the calculation load of the baseline SSD network. Then the color detection algorithm based on the phase difference method and the connected component calculation are used to further filter the detection results, and finally, the data enhancement strategy based on the image appearance transformation is used to improve the balance of the dataset. The experimental results show that the proposed method is 3% more accurate than the baseline SSD network, and more importantly, the detection speed is also increased by 1.2 times. [ABSTRACT FROM AUTHOR]

In this study we investigated the antimicrobial activity of magnolol on Staphylococcus aureus. The minimal inhibitory concentrations of magnolol against 31 S. aureus strains ranged from 4-32 microg/mL. In addition, hemolysin assays, Western blotting, and real-time RT-PCR were performed to investigate the effect of magnolol on alpha-toxin secretion by both methicillin-sensitive S. aureus (MSSA) and methicillin-resistant S. aureus (MRSA). The results indicated that sub-inhibitory concentrations of magnolol dose-dependently inhibited the transcription of hla (the gene encoding alpha-toxin) in S. aureus, resulting in a reduction of alpha-toxin secretion and, thus, hemolytic activities.