2.2 GPa ultra-strong nanostructured steel with unexpected large ductility

Conferencia del Programa Embajadores del CENIMFor centuries, the quest for achieving high performance bulk metals and alloys for structural applications has been perplexed by the notorious trade-off between strength and ductility. Here we report a conceptually novel strategy to resolve this dilemma by incorporating all available strengthening and plasticity mechanisms at multiple length scales. This approach is a paradigm shift in structural alloy design and offers a steel with a yield strength of 2.2 GPa and an unexpected tensile uniform elongation of 16%. To our best knowledge, this steel stands the best bulk metallic alloys so far in terms of yield strength-uniform elongation combination. More importantly, this steel was produced using conventional thermal mechanical processing routes (i.e. rolling and annealing), which will facilitate its future industrial mass production and applications in a wide spectrum of industrial settings. The present novel steel possesses a dual-phase hierarchical nanostructure consisting of nanosized precipitates, nanotwins, and lamellar nano- and ultrafine-grained austenite and ferrite. This novel steel has been strengthened by all the available strengthening mechanisms, including solid-solution, precipitation, dislocations, grain boundary and twin boundary strengthening. Besides dislocation plasticity, the novel steel also experiences transformation-induced plasticity (TRIP) as well as twinning-induced plasticity (TWIP). More importantly, different to other lab-scale methods of producing ultrahigh strength alloys, such as the severe plasticity deformation (SPD) technique, the present novel steel has been produced by conventional processing routes currenlty used in the steel industry. No additional facilities are required for the steel industry to produce this novel steel, which will facilitate its future industrial application.Proyecto i-Link0876 "Design and Nano/Microcharacterization of Quenched and Partitioned (Q&P) Martensitic Stainless Steels".N

Design and model analysis of the sonic vibration head

As a novel environmental sampling technique, sonic vibration drilling has been rapidly developed in the past few years. The penetration force is generated from two eccentric shafts driven by hydraulic motors. This gives rise to the vertical oscillation of the drill pipe to drill in the stratum. As the most important parts of the sonic driller, the vibration head consists of eccentric structure, synchronization mechanism, supporting structure and rotating structure. In the first part of this paper, a 3D mathematical model was developed after analyzing the working law of sonic vibration head by using SolidWork. In the second part, the model was stimulated in order to predict the performances of the sonic vibration head by using ANASYS. In the third part, a physical prototype was developed to conduct practical experiments, confirming feasibility of the previous design and stimulation, and making good references for future optimization

KINEMATICS ANALYSIS OF CHINESE VAULTING HORSE ATHLETE YANG YAHONG'S ACTION OF "HANDSPRING WITH 90&ORDM-180&ORDM-LAYOUT BACK DOUBLE TWIST"

"Handspring with 90&ordm-180&ordm-layout back double twists" is the new developed kind of action in the women gymnastics championship in recent years. Yang Yahong is the only one that can complete this action. With the method of sports biomechanics, the kinematics characteristic of her action will be analyzed and her advantage and disadvantage will be exposed. The finding shows that her running up is fast, the angle speed of the handspring in the second flight is swift and the pose in space is excellent. However, the amount of time of pushing horse is over long and the height of the 'handspring in the second flight is not enough

Development of Downhole Motor Drilling Test Platform

AbstractThe Downhole motor is a kind of important rotary or percussive power drilling tool driven by high pressure mud. Drilling using downhole motor can reduce the energy consumption caused by the friction between long drill string and borehole, and reduce drill pipe wear. In this paper, some important drilling simulation experimental devices around the world have been studied, especially, two kind of drilling simulation experimental devices, the conventional bottom hole experimental device and high temperature and high pressure experimental devices have been analyzed respectively. At home and abroad, the typical drilling simulation devices include ZM-35, LST-10, LMT-I, M150, and Terra Tek, etc.. The characters, structures, principles and experimental methods of these typical simulation devices had been introduced in detail, which provides a reference for developing downhole motor testing and drilling process testing

Hierarchical Side-Tuning for Vision Transformers

Fine-tuning pre-trained Vision Transformers (ViT) has consistently demonstrated promising performance in the realm of visual recognition. However, adapting large pre-trained models to various tasks poses a significant challenge. This challenge arises from the need for each model to undergo an independent and comprehensive fine-tuning process, leading to substantial computational and memory demands. While recent advancements in Parameter-efficient Transfer Learning (PETL) have demonstrated their ability to achieve superior performance compared to full fine-tuning with a smaller subset of parameter updates, they tend to overlook dense prediction tasks such as object detection and segmentation. In this paper, we introduce Hierarchical Side-Tuning (HST), a novel PETL approach that enables ViT transfer to various downstream tasks effectively. Diverging from existing methods that exclusively fine-tune parameters within input spaces or certain modules connected to the backbone, we tune a lightweight and hierarchical side network (HSN) that leverages intermediate activations extracted from the backbone and generates multi-scale features to make predictions. To validate HST, we conducted extensive experiments encompassing diverse visual tasks, including classification, object detection, instance segmentation, and semantic segmentation. Notably, our method achieves state-of-the-art average Top-1 accuracy of 76.0% on VTAB-1k, all while fine-tuning a mere 0.78M parameters. When applied to object detection tasks on COCO testdev benchmark, HST even surpasses full fine-tuning and obtains better performance with 49.7 box AP and 43.2 mask AP using Cascade Mask R-CNN

ESTextSpotter: Towards Better Scene Text Spotting with Explicit Synergy in Transformer

In recent years, end-to-end scene text spotting approaches are evolving to the Transformer-based framework. While previous studies have shown the crucial importance of the intrinsic synergy between text detection and recognition, recent advances in Transformer-based methods usually adopt an implicit synergy strategy with shared query, which can not fully realize the potential of these two interactive tasks. In this paper, we argue that the explicit synergy considering distinct characteristics of text detection and recognition can significantly improve the performance text spotting. To this end, we introduce a new model named Explicit Synergy-based Text Spotting Transformer framework (ESTextSpotter), which achieves explicit synergy by modeling discriminative and interactive features for text detection and recognition within a single decoder. Specifically, we decompose the conventional shared query into task-aware queries for text polygon and content, respectively. Through the decoder with the proposed vision-language communication module, the queries interact with each other in an explicit manner while preserving discriminative patterns of text detection and recognition, thus improving performance significantly. Additionally, we propose a task-aware query initialization scheme to ensure stable training. Experimental results demonstrate that our model significantly outperforms previous state-of-the-art methods. Code is available at https://github.com/mxin262/ESTextSpotter.Comment: Accepted to ICCV 202

SPTS v2: Single-Point Scene Text Spotting

End-to-end scene text spotting has made significant progress due to its intrinsic synergy between text detection and recognition. Previous methods commonly regard manual annotations such as horizontal rectangles, rotated rectangles, quadrangles, and polygons as a prerequisite, which are much more expensive than using single-point. For the first time, we demonstrate that training scene text spotting models can be achieved with an extremely low-cost single-point annotation by the proposed framework, termed SPTS v2. SPTS v2 reserves the advantage of the auto-regressive Transformer with an Instance Assignment Decoder (IAD) through sequentially predicting the center points of all text instances inside the same predicting sequence, while with a Parallel Recognition Decoder (PRD) for text recognition in parallel. These two decoders share the same parameters and are interactively connected with a simple but effective information transmission process to pass the gradient and information. Comprehensive experiments on various existing benchmark datasets demonstrate the SPTS v2 can outperform previous state-of-the-art single-point text spotters with fewer parameters while achieving 19$\times$ faster inference speed. Most importantly, within the scope of our SPTS v2, extensive experiments further reveal an important phenomenon that single-point serves as the optimal setting for the scene text spotting compared to non-point, rectangular bounding box, and polygonal bounding box. Such an attempt provides a significant opportunity for scene text spotting applications beyond the realms of existing paradigms. Code will be available at https://github.com/bytedance/SPTSv2.Comment: arXiv admin note: text overlap with arXiv:2112.0791

Fretting wear-induced sudden loss of corrosion resistance in a corrosion-resistant Ni-based alloy

The fretting corrosion testing of A690 with a sliding amplitude of 100 μm and a normal force of 20 N was conducted in a manually designed equipment with a tube-on-plate contact configuration exposed to simulated pressurized water reactor (PWR) secondary water. While A690 exhibits superior corrosion resistance, the fretting wear results in a two orders of magnitude faster oxidation rate at the contacting surface. A detailed characterization reveals that the fretting wear-induced dynamic stress/strain does not only constantly break the integrity of the oxide scale but also accelerate the consumption of Cr in the near-surface metal matrix. The degradation of corrosion resistance of A690 under fretting wear can be mainly attributed to 1) the decrease of oxide scale thickness; 2) the introduction of nanocavities and nano-cracks at the grain boundaries of the oxide scale; 3) the formation of a nano-grained Cr-depleted matrix zone under the oxide scale. These findings suggest that, for the structural alloys serviced at elevated temperatures, even a small amplitude motion with low normal stress against their contacting surface could significantly deteriorate their corrosion resistance. Hence, the service lifetime of the structural alloys needs to be reconsidered once friction is existing

Impact of chest pain center quality control indicators on mortality risk in ST-segment elevation myocardial infarction patients: a study based on Killip classification

BackgroundDespite the crucial role of Chest pain centers (CPCs) in acute myocardial infarction (AMI) management, China's mortality rate for ST-segment elevation myocardial infarction (STEMI) has remained stagnant. This study evaluates the influence of CPC quality control indicators on mortality risk in STEMI patients receiving primary percutaneous coronary intervention (PPCI) during the COVID-19 pandemic.MethodsA cohort of 664 consecutive STEMI patients undergoing PPCI from 2020 to 2022 was analyzed using Cox proportional hazards regression models. The cohort was stratified by Killip classification at admission (Class 1: n = 402, Class ≥2: n = 262).ResultsAt a median follow-up of 17 months, 35 deaths were recorded. In Class ≥2, longer door-to-balloon (D-to-B) time, PCI informed consent time, catheterization laboratory activation time, and diagnosis-to-loading dose dual antiplatelet therapy (DAPT) time were associated with increased mortality risk. In Class 1, consultation time (notice to arrival) under 10 min reduced death risk. In Class ≥2, PCI informed consent time under 20 min decreased mortality risk.ConclusionCPC quality control metrics affect STEMI mortality based on Killip class. Key factors include time indicators and standardization of CPC management. The study provides guidance for quality care during COVID-19