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Wire Arc Additive Manufacturing (WAAM) has many applications in fabricating complex metal parts. However, controlling surface roughness is very challenging in WAAM processes. Typically, machining methods are applied to reduce the surface roughness after a part is fabricated, which is costly and ineffective. Therefore, controlling the WAAM process parameters to achieve better surface roughness is important. This paper proposes a machine learning method based on Gaussian Process Regression to construct a model between the WAAM process parameters and top surface roughness. In order to measure the top surface roughness of a manufactured part, a 3D laser measurement system is developed. The experimental datasets are collected and then divided into training and testing datasets. A top surface roughness model is then constructed using the training datasets and verified using the testing datasets. Experimental results demonstrate that the proposed method achieves less than 50 μm accuracy in surface roughness prediction.

Timely and accurate flame detection is a very important and practical technology for preventing the occurrence of fire accidents effectively. However, the current methods of flame detection are still faced with many challenges in video surveillance scenarios due to issues such as varying flame shapes, imbalanced samples, and interference from flame-like objects. In this work, a real-time flame detection method based on deformable object detection and time sequence analysis is proposed to address these issues. Firstly, based on the existing single-stage object detection network YOLOv5s, the network structure is improved by introducing deformable convolution to enhance the feature extraction ability for irregularly shaped flames. Secondly, the loss function is improved by using Focal Loss as the classification loss function to solve the problems of the imbalance of positive (flames) and negative (background) samples, as well as the imbalance of easy and hard samples, and by using EIOU Loss as the regression loss function to solve the problems of a slow convergence speed and inaccurate regression position in network training. Finally, a time sequence analysis strategy is adopted to comprehensively analyze the flame detection results of the current frame and historical frames in the surveillance video, alleviating false alarms caused by flame shape changes, flame occlusion, and flame-like interference. The experimental results indicate that the average precision (AP) and the F-Measure index of flame detection using the proposed method reach 93.0% and 89.6%, respectively, both of which are superior to the compared methods, and the detection speed is 24–26 FPS, meeting the real-time requirements of video flame detection.

Non-heme iron and α-ketoglutarate-dependent (Fe/αKG) oxygenases catalyze various oxidative biotransformations. Due to their catalytic flexibility and high efficiency, Fe/αKG oxygenases have attracted keen attention for their application as biocatalysts. Here, we report the biochemical and structural characterizations of the unusually promiscuous and catalytically versatile Fe/αKG oxygenase SptF, involved in the biosynthesis of fungal meroterpenoid emervaridones. The in vitro analysis revealed that SptF catalyzes several continuous oxidation reactions, including hydroxylation, desaturation, epoxidation, and skeletal rearrangement. SptF exhibits extremely broad substrate specificity toward various meroterpenoids, and efficiently produced unique cyclopropane-ring-fused 5/3/5/5/6/6 and 5/3/6/6/6 scaffolds from terretonins. Moreover, SptF also hydroxylates steroids, including androsterone, testosterone, and progesterone, with different regiospecificities. Crystallographic and structure-based mutagenesis studies of SptF revealed the molecular basis of the enzyme reactions, and suggested that the malleability of the loop region contributes to the remarkable substrate promiscuity. SptF exhibits great potential as a promising biocatalyst for oxidation reactions. Non-heme iron and α-ketoglutarate-dependent (Fe/αKG) oxygenases have attracted attention for their application as biocatalysts due to their flexibility and high efficiency. Here, the authors show the biochemical and structural characterizations of the versatile Fe/αKG oxygenase SptF, involved in the biosynthesis of fungal meroterpenoid emervaridones.

Background Microbiome assembly in early life may have a long-term impact on host health. Larval nursery is a crucial period that determines the success in culture of Litopenaeus vannamei , the most productive shrimp species in world aquaculture industry. However, the succession patterns and assembly mechanisms of larval shrimp bacterial community still lack characterization at a fine temporal scale. Here, using a high-frequency sampling strategy and 16S rRNA gene amplicon sequencing, we investigated dynamics of larval shrimp bacterial community and its relationship with bacterioplankton in the rearing water across the whole developmental cycle in a realistic aquaculture practice. Results Alpha-diversity of larval shrimp bacteria showed a U-shaped pattern across the developmental cycle with the stages zoea and mysis as the valley. Correspondingly, the compositions of dominant bacterial taxa at the stages nauplius and early postlarvae were more complex than other stages. Remarkably, Rhodobacteraceae maintained the overwhelming dominance after the mouth opening of larvae ( zoea I~early postlarvae ). The taxonomic and phylogenetic compositions of larval bacterial community both showed stage-dependent patterns with higher rate of taxonomic turnover, suggesting that taxonomic turnover was mainly driven by temporal switching among closely related taxa (such as Rhodobacteraceae taxa). The assembly of larval bacteria was overall governed by neutral processes (dispersal among individuals and ecological drift) at all the stages, but bacterioplankton also had certain contribution during three sub-stages of zoea , when larval and water bacterial communities were most associated. Furthermore, the positive host selection for Rhodobacteraceae taxa from the rearing water during the zoea stage and its persistent dominance and large predicted contribution to metabolic potentials of organic matters at post-mouth opening stages suggest a crucial role of this family in larval microbiome and thus a potential source of probiotic candidates for shrimp larval nursery. Conclusions Our results reveal pronounced succession patterns and dynamic assembly processes of larval shrimp bacterial communities during the developmental cycle, highlighting the importance of the mouth opening stage from the perspective of microbial ecology. We also suggest the possibility and potential timing in microbial management of the rearing water for achieving the beneficial larval microbiota in the nursery practice. EV4FFVE7Wr7jezsPpZE3f6 Video Abstract

Background Inflammatory Bowel Disease (IBD) affects reproductive-aged women. Active disease can lead to decreased fertility. Although the vast majority of international guidelines recommend for the continuation of anti-TNF-α during pregnancy, recent studies have raised concerns about the safety of anti-tumor necrosis factor-α (TNF-α) therapy during pregnancy, both for patients and for physicians. Methods Studies that evaluate the safety of anti-TNF-α therapy in pregnant women with IBD were identified using bibliographical searches. An updated meta-analysis was performed for pregnancy outcomes, such as live birth, abortion, still birth, preterm birth, low birth weight, congenital abnormalities, and neonatal infection. Odds ratio (OR) with 95% confidence interval (CI) are reported. Data on disease activity, timing of anti-TNF-α therapy were collected for further analysis. Results Overall, 11 studies were screened from on-line databases and international meeting abstracts. An increased risk of abortion (OR, 1.33; 95% CI, 1.02–1.74; P  = 0.04) and preterm birth (OR, 1.16; 95% CI, 1.05–1.28; P  = 0.004), and a decreased risk of live birth (OR, 0.83; 95% CI, 0.74–0.94; P  = 0.002]) were found in the anti-TNF-α therapy group compared with the control group (no use of anti-TNF-α therapy). The subgroup analyses based on the disease activity showed there is no significant association between the use of anti-TNF-α therapy during pregnancy on adverse pregnancy outcomes of abortion, preterm birth, and live birth. The rates of still birth, low birth weight, and congenital abnormalities in the anti-TNF-α therapy group were not significantly different from those in the control group. Conclusions Anti-TNF-α therapy does not increase the risks of still birth, low birth weight, and congenital abnormalities; however it may be assicated with increased risks of abortion and preterm birth, which are accompanied by a lower rate of live birth. Although these findings may be confounding by potential disease activity, they offer some opposite viewpoints with biologic agent use. Therefore, more studies are required to further confirm the safety of anti-TNF-α therapy in pregnancy with IBD.

Laser speckle contrast imaging (LSCI) is a full-field, high spatiotemporal resolution and low-cost optical technique for measuring blood flow, which has been successfully used for neurovascular imaging. However, due to the low signal–noise ratio and the relatively small sizes, segmenting the cerebral vessels in LSCI has always been a technical challenge. Recently, deep learning has shown its advantages in vascular segmentation. Nonetheless, ground truth by manual labeling is usually required for training the network, which makes it difficult to implement in practice. In this manuscript, we proposed a deep learning-based method for real-time cerebral vessel segmentation of LSCI without ground truth labels, which could be further integrated into intraoperative blood vessel imaging system. Synthetic LSCI images were obtained with a synthesis network from LSCI images and public labeled dataset of Digital Retinal Images for Vessel Extraction, which were then used to train the segmentation network. Using matching strategies to reduce the size discrepancy between retinal images and laser speckle contrast images, we could further significantly improve image synthesis and segmentation performance. In the testing LSCI images of rodent cerebral vessels, the proposed method resulted in a dice similarity coefficient of over 75%.

Automatic trajectory generation for spray painting is highly desirable for today’s automotive manufacturing. Generating paint gun trajectories for free-form surfaces to satisfy paint thickness requirements is still highly challenging due to the complex geometry of free-form surfaces. In this paper, a CAD-guided paint gun trajectory generation system for free-form surfaces has been developed. The system utilizes the CAD information of a free-form surface and a paint gun model to automatically generate a paint gun trajectory to satisfy the paint thickness requirements. Complex surfaces are divided into patches to satisfy the constraints. A trajectory integration algorithm is developed to integrate the trajectories of the patches. The paint thickness deviation from the required paint thickness is optimized by modifying the paint gun velocity. A paint thickness verification method is also developed to verify the generated trajectories. The results of simulations have shown that the trajectory generation system achieves satisfactory performance. This trajectory generation system can also be applied to generate trajectories for many other CAD-guided robot trajectory planning applications in surface manufacturing.

The development of controlled-release fertilizers (CRFs) has faced significant challenges due to high hydrophilicity and short release lifespan of bio-based materials, as well as non-renewable and high cost of polyester polyols (PPs). In this study, lignin-based polyols (LPs) and PPs were modified to form a cross-linked polymer film on the surface of urea through an in situ reaction. This approach effectively balanced the slow-release ability and environmental protection of controlled-release fertilizer films. A two-factor, five-level orthogonal test was designed for the mass ratio of lignin/polyester polyol and polyol/polyaryl polymethylene isocyanate (PAPI), comprising a total of 25 treatments. The results indicated that the appropriateness of lignin polyols increased the hydrogen bond content of polyurethane membrane, improved the mechanical strength of the fertilizer membrane shell, and effectively reduced friction losses during storage and transportation. Moreover, optimizing the polyol-to-PAPI ratio minimized coating porosity, produced a smoother and denser surface, and prolonged the nitrogen release period. When the lignin polyol dosage was 25% and the polyol to PAPI ratio was 1:2, the nitrogen release time of the prepared coated urea extended to 32 days, which was 3.5 times longer than that of lignin polyurethane coated urea (7 days). The incorporation of lignin and the optimal ratio of coating materials significantly improved the controlled-release efficiency of coated fertilizer, providing theoretical support for the sustainable agricultural application of biomass.

Asphalt mixture proportion design is one of the most important steps in asphalt pavement design and application. This study proposes a novel multi-objective particle swarm optimization (MOPSO) algorithm employing the Gaussian process regression (GPR)-based machine learning (ML) method for multi-variable, multi-level optimization problems with multiple constraints. First, the GPR-based ML method is proposed to model the objective and constraint functions without the explicit relationships between variables and objectives. In the optimization step, the metaheuristic algorithm based on adaptive weight multi-objective particle swarm optimization (AWMOPSO) is used to achieve the global optimal solution, which is very efficient for the objectives and constraints without mathematical relationships. The results showed that the optimal GPR model could describe the relationship between variables and objectives well in terms of root-mean-square error (RMSE) and R2. After the optimization by the proposed GPR-AWMOPSO algorithm, the comprehensive pavement performances were enhanced in terms of the permanent deformation resistance at high temperature, crack resistance at low temperature as well as moisture stability. Therefore, the proposed GPR-AWMOPSO algorithm is the best option and efficient for maximizing the performances of composite modified asphalt mixture. The GPR-AWMOPSO algorithm has advantages of less computational time and fewer samples, higher accuracy, etc. over traditional laboratory-based experimental methods, which can serve as guidance for the proportion optimization design of asphalt pavement.

Perfluorinated compounds (PFCs) are emerging environmental pollutants characterized by their extreme stability and resistance to degradation. Among them, tetrafluoromethane (CF4) is the simplest and most abundant PFC in the atmosphere. However, the highest C─F bond energy and its highly symmetrical structure make it particularly challenging to decompose. In this work, a yolk–shell Al2O3 micro‐reactor is developed to enhance the catalytic hydrolysis performance of CF4 by creating a local autothermic environment. Finite element simulations predict that the yolk–shell Al2O3 micro‐reactor captures the heat released during the catalytic hydrolysis of CF4, resulting in a local autothermic environment within the yolk–shell structure that is 50 °C higher than the set temperature. The effectiveness of this local autothermic environment is experimentally confirmed by in situ Raman spectroscopy. As a result, the obtained yolk–shell Al2O3 micro‐reactor achieves 100% CF4 conversion at a considerably low temperature of 580 °C for over 150 h, while hollow and solid Al2O3 structures required higher temperatures of 610 and 630 °C, respectively, to achieve the same conversion rate, demonstrating the potential of yolk–shell Al2O3 micro‐reactor to significantly reduce the energy requirements for PFCs degradation and contribute to more sustainable and effective environmental remediation strategies. The yolk–shell micro‐reactor captures the heat released during the catalytic hydrolysis of tetrafluoromethane (CF4) to create a local autothermic environment between the yolk and the shell, increasing the local temperature by 50 °C. As a result, the yolk–shell micro‐reactor achieved 100% CF4 degradation at a considerably low temperature of 580 °C for over 150 h.