Atomic Mechanism and Criterion for Hydrogen-Induced Transgranular to Intergranular Fracture Transition

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The Uncertainty of Roughness and Its Influence on Dynamic Response and Performance of Canal System

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Bis[(2-pyrid­yl)(2-pyridyl­amino)­methano­lato]manganese(III) nitrate

The MnIII atom in the title complex, [Mn(C11H10N3O)2]NO3, is coordinated by the two tridentate (2-pyrid­yl)(2-pyridyl­amino)­methano­late ligands, forming a six-coordinate environment. The four pyridyl N atoms constitute the equatorial plane on which the manganese(III) ion lies; the coordination plane suffers a slight distortion as indicated by the average plane deviation of 0.058 Å. The methano­late O atoms occupy the axial positions. The coordination geometry is thus octa­hedral. In the title compound, the cations are linked by nitrate anions via N—H⋯O hydrogen bonds to form one-dimensional chains. Moreover, the one-dimensional structure is stabilized by inter­molecular edge-to-face aromatic π–π inter­actions with a center-of-inversion at a distance of ca 4.634 Å

POMP: Probability-driven Meta-graph Prompter for LLMs in Low-resource Unsupervised Neural Machine Translation

Low-resource languages (LRLs) face challenges in supervised neural machine translation due to limited parallel data, prompting research into unsupervised methods. Unsupervised neural machine translation (UNMT) methods, including back-translation, transfer learning, and pivot-based translation, offer practical solutions for LRL translation, but they are hindered by issues like synthetic data noise, language bias, and error propagation, which can potentially be mitigated by Large Language Models (LLMs). LLMs have advanced NMT with in-context learning (ICL) and supervised fine-tuning methods, but insufficient training data results in poor performance in LRLs. We argue that LLMs can mitigate the linguistic noise with auxiliary languages to improve translations in LRLs. In this paper, we propose Probability-driven Meta-graph Prompter (POMP), a novel approach employing a dynamic, sampling-based graph of multiple auxiliary languages to enhance LLMs' translation capabilities for LRLs. POMP involves constructing a directed acyclic meta-graph for each source language, from which we dynamically sample multiple paths to prompt LLMs to mitigate the linguistic noise and improve translations during training. We use the BLEURT metric to evaluate the translations and back-propagate rewards, estimated by scores, to update the probabilities of auxiliary languages in the paths. Our experiments show significant improvements in the translation quality of three LRLs, demonstrating the effectiveness of our approach

Recursively Summarizing Enables Long-Term Dialogue Memory in Large Language Models

Most open-domain dialogue systems suffer from forgetting important information, especially in a long-term conversation. Existing works usually train the specific retriever or summarizer to obtain key information from the past, which is time-consuming and highly depends on the quality of labeled data. To alleviate this problem, we propose to recursively generate summaries/ memory using large language models (LLMs) to enhance long-term memory ability. Specifically, our method first stimulates LLMs to memorize small dialogue contexts and then recursively produce new memory using previous memory and following contexts. Finally, the LLM can easily generate a highly consistent response with the help of the latest memory. We evaluate our method using ChatGPT and text-davinci-003, and the experiments on the widely-used public dataset show that our method can generate more consistent responses in a long-context conversation. Notably, our method is a potential solution to enable the LLM to model the extremely long context. Code and scripts will be released later

Self-Evolution Learning for Mixup: Enhance Data Augmentation on Few-Shot Text Classification Tasks

Text classification tasks often encounter few shot scenarios with limited labeled data, and addressing data scarcity is crucial. Data augmentation with mixup has shown to be effective on various text classification tasks. However, most of the mixup methods do not consider the varying degree of learning difficulty in different stages of training and generate new samples with one hot labels, resulting in the model over confidence. In this paper, we propose a self evolution learning (SE) based mixup approach for data augmentation in text classification, which can generate more adaptive and model friendly pesudo samples for the model training. SE focuses on the variation of the model's learning ability. To alleviate the model confidence, we introduce a novel instance specific label smoothing approach, which linearly interpolates the model's output and one hot labels of the original samples to generate new soft for label mixing up. Through experimental analysis, in addition to improving classification accuracy, we demonstrate that SE also enhances the model's generalize ability

Bis(μ-2-{[2-(1,3-benzothia­zol-2-yl)hydrazinyl­idene]meth­yl}-6-meth­oxy­phenolato)bis­[dinitratodysprosium(III)] methanol disolvate

In the centrosymmetric dinuclear title compound, [Dy2(C15H12N3O2S)2(NO3)4]·2CH3OH, the two DyIII atoms are coordinated by two deprotonated 2-{[2-(1,3-benzothia­zol-2-yl)hydrazinyl­idene]meth­yl}-6-meth­oxy­phenol ligands and four nitrate ions, all of which are chelating. The crystal packing is stabilized by inter­molecular N—H⋯O hydrogen bonds and weak O—H⋯O inter­actions, forming a two-dimensional network parallel to (010)

Effect of heat input on nanomechanical properties of wire-arc additive manufactured Al 4047 alloys

Heat input is one of the most important process parameters during additive manufacturing (AM). It is of great significance to understand the effect of heat input on the microstructure and nanomechanical properties, as well as the underlying mechanisms. Wire-arc additive manufactured (WAAM-ed) Al 4047 alloys under different heat inputs were produced and studied in this work. The as-manufactured Al alloys showed hypoeutectic microstructure that consisted of primary Al (α-Al) dendrite and ultrafine Al–Si eutectic. The effect of heat input on hardness and strain rate sensitivity (SRS) were investigated through nanoindentation. The nanohardness decreased with the increasing heat input, in accordance with the trend of yield strength and microhardness in the previous studies, in which the mechanism was usually explained by the grain growth model and Hall-Petch relationship. This work suggests a distinct mechanism regarding the effect of heat input on nanohardness, which is the enhanced solid solution strengthening produced by lower heat input. In addition, the heat input had little effect on the SRS and activation volume. It is hoped that this study leads to new insights into the understanding of the relation between heat input and nanomechanical properties, and further benefits to improve the targeted mechanical properties and engineering applications of the AM-ed materials.publishedVersio

Experiment of Carbonate Dissolution: Implication for High Quality Carbonate Reservoir Formation in Deep and Ultradeep Basins

As the most frontiers in petroleum geology, the study of dissolution-based rock formation in deep carbonate reservoirs provides insight into pore development mechanism of petroleum reservoir space, while predicting reservoir distribution in deep-ultradeep layers. In this study, we conducted dissolution-precipitation experiments simulating surface to deep burial environments (open and semiopen systems). The effects of temperature, pressure, and dissolved ions on carbonate dissolution-precipitation were investigated under high temperature and pressure (~200°C; ~70 Mpa) with a series of petrographic and geochemical analytical methods. The results showed that the window-shape dissolution curve appeared in 75~150°C in the open system and 120~175°C in the semiopen system. Furthermore, the dissolution weight loss of carbonate rocks in the open system was higher than that of semiopen system, making it more favorable for gaining porosity. The type of fluid and rock largely determines the reservoir quality. In the open system, the dissolution weight loss of calcite was higher than that of dolomite with 0.3% CO2 as the reaction fluid. In the semiopen system, the weight loss from dolomitic limestone prevailed with 0.3% CO2 as the reaction fluid. Our study could provide theoretical basis for the prediction of high quality carbonate reservoirs in deep and ultradeep layers