Applications of Oxyhydrogen, Direct Water Injection, and Early-Intake Valve Closure Technologies on a Petrol Spark Ignition Engine—A Path towards Zero-Emission Hydrogen Internal Combustion Engines

This study investigates the performance of a 4-MIX engine utilizing hydrogen combustion in pure oxygen, water injection, and the application of the early-intake valve closure (EIVC) Miller cycle. Transitioning from a standard petrol–oil mix to hydrogen fuel with pure oxygen combustion aims to reduce emissions. Performance comparisons between baseline and oxyhydrogen engines showed proportional growth in the energy input rate with increasing rotational speed. The oxyhydrogen engine exhibited smoother reductions in brake torque and thermal efficiency as rotational speed increased compared to the baseline, attributed to hydrogen’s higher heating value. Water injection targeted cylinder and exhaust temperature reduction while maintaining a consistent injected mass. The results indicated a threshold of around 2.5 kg/h for the optimal water injection rate, beyond which positive effects on engine performance emerged. Investigation into the EIVC Miller cycle revealed improvements in brake torque, thermal efficiency, and brake specific fuel consumption as early-intake valve closure increased. Overall, the EIVC model exhibited superior energy efficiency, torque output, and thermal efficiency compared to alternative models, effectively addressing emissions and cylinder temperature concerns

Comparison and phylogenetic analysis based on the B2L gene of orf virus from goats and sheep in China during 2009-2011

As a zoonotic infectious disease, orf outbreaks have been reported in China in recent years. However, molecular epidemiology analysis has not been performed for Chinese orf virus (ORFV) strains. Here, we have identified 13 ORFVs from goats and sheep in China between 2009 and 2011. Thirty-four complete B2L sequences were used to construct a phylogenetic tree to elucidate the molecular epidemiology of ORFV in China. Nucleotide sequences of B2L genes of clinical samples and attenuated vaccine strains were aligned and compared. Three genotypes were found by molecular epidemiology analysis. Amino acid substitutions were dispersed among B2 polypeptides from wild and attenuated ORFV strains. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00705-013-1946-6) contains supplementary material, which is available to authorized users

Networks are Slacking Off: Understanding Generalization Problem in Image Deraining

Deep deraining networks, while successful in laboratory benchmarks, consistently encounter substantial generalization issues when deployed in real-world applications. A prevailing perspective in deep learning encourages the use of highly complex training data, with the expectation that a richer image content knowledge will facilitate overcoming the generalization problem. However, through comprehensive and systematic experimentation, we discovered that this strategy does not enhance the generalization capability of these networks. On the contrary, it exacerbates the tendency of networks to overfit to specific degradations. Our experiments reveal that better generalization in a deraining network can be achieved by simplifying the complexity of the training data. This is due to the networks are slacking off during training, that is, learning the least complex elements in the image content and degradation to minimize training loss. When the complexity of the background image is less than that of the rain streaks, the network will prioritize the reconstruction of the background, thereby avoiding overfitting to the rain patterns and resulting in improved generalization performance. Our research not only offers a valuable perspective and methodology for better understanding the generalization problem in low-level vision tasks, but also displays promising practical potential

A High-Accuracy Algorithm for Surface Defect Detection of Steel Based on DAG-SVM

The quality of the steel surface is a crucial parameter. An improved method based on machine vision for steel surface defects detection is proposed. The experiment is based on 20 images for each of 6 distinct steel defects, a total of 120 defective images achieved from the detection system. 128 different features are extracted from the images and feature dimensions are reduced by the principle component analysis (PCA) based on the sample correlation coefficient matrix. Hierarchical clustering by Euclidean distance is implemented to find defect characteristics differentiation, the steel surface defects are classified based on directed acyclic graph support vector machine (DAG-SVM). The experimental results indicate that this method can recognize more than 98 % of the steel surface defects at a faster speed that can meet the demands on the steel surface quality online detection

A High-Accuracy Algorithm for Surface Defect Detection of Steel Based on DAG-SVM

The quality of the steel surface is a crucial parameter. An improved method based on machine vision for steel surface defects detection is proposed. The experiment is based on 20 images for each of 6 distinct steel defects, a total of 120 defective images achieved from the detection system. 128 different features are extracted from the images and feature dimensions are reduced by the principle component analysis (PCA) based on the sample correlation coefficient matrix. Hierarchical clustering by Euclidean distance is implemented to find defect characteristics differentiation, the steel surface defects are classified based on directed acyclic graph support vector machine (DAG-SVM). The experimental results indicate that this method can recognize more than 98 % of the steel surface defects at a faster speed that can meet the demands on the steel surface quality online detection

HAT: Hybrid Attention Transformer for Image Restoration

Transformer-based methods have shown impressive performance in image restoration tasks, such as image super-resolution and denoising. However, we find that these networks can only utilize a limited spatial range of input information through attribution analysis. This implies that the potential of Transformer is still not fully exploited in existing networks. In order to activate more input pixels for better restoration, we propose a new Hybrid Attention Transformer (HAT). It combines both channel attention and window-based self-attention schemes, thus making use of their complementary advantages. Moreover, to better aggregate the cross-window information, we introduce an overlapping cross-attention module to enhance the interaction between neighboring window features. In the training stage, we additionally adopt a same-task pre-training strategy to further exploit the potential of the model for further improvement. Extensive experiments have demonstrated the effectiveness of the proposed modules. We further scale up the model to show that the performance of the SR task can be greatly improved. Besides, we extend HAT to more image restoration applications, including real-world image super-resolution, Gaussian image denoising and image compression artifacts reduction. Experiments on benchmark and real-world datasets demonstrate that our HAT achieves state-of-the-art performance both quantitatively and qualitatively. Codes and models are publicly available at https://github.com/XPixelGroup/HAT.Comment: Extended version of HA

Iron Metabolism Regulates p53 Signaling through Direct Heme-p53 Interaction and Modulation of p53 Localization, Stability, and Function

Iron excess is closely associated with tumorigenesis in multiple types of human cancers, with underlying mechanisms yet unclear. Recently, iron deprivation has emerged as a major strategy for chemotherapy, but it exerts tumor suppression only on select human malignancies. Here, we report that the tumor suppressor protein p53 is downregulated during iron excess. Strikingly, the iron polyporphyrin heme binds to p53 protein, interferes with p53-DNA interactions, and triggers both nuclear export and cytosolic degradation of p53. Moreover, in a tumorigenicity assay, iron deprivation suppressed wild-type p53-dependent tumor growth, suggesting that upregulation of wild-type p53 signaling underlies the selective efficacy of iron deprivation. Our findings thus identify a direct link between iron/heme homeostasis and the regulation of p53 signaling, which not only provides mechanistic insights into iron-excess-associated tumorigenesis but may also help predict and improve outcomes in iron-deprivation-based chemotherapy