Modeling the influence of carbon branching structure on secondary organic aerosol formation via multiphase reactions of alkanes

Branched alkanes represent a significant proportion of hydrocarbons emitted in urban environments. To accurately predict the secondary organic aerosol (SOA) budgets in urban environments, these branched alkanes should be considered as SOA precursors. However, the potential to form SOA from diverse branched alkanes under varying environmental conditions is currently not well understood. In this study, the Unified Partitioning Aerosol Phase Reaction (UNIPAR) model is extended to predict SOA formation via the multiphase reactions of various branched alkanes. Simulations with the UNIPAR model, which processes multiphase partitioning and aerosol-phase reactions to form SOA, require a product distribution predicted from an explicit gas kinetic mechanism, whose oxygenated products are applied to create a volatility- and reactivity-based αi species array. Due to a lack of practically applicable explicit gas mechanisms, the prediction of the product distributions of various branched alkanes was approached with an innovative method that considers carbon lengths and branching structures. The αi array of each branched alkane was primarily constructed using an existing αi array of the linear alkane with the nearest vapor pressure. Generally, the vapor pressures of branched alkanes and their oxidation products are lower than those of linear alkanes with the same carbon number. In addition, increasing the number of alkyl branches can also decrease the ability of alkanes to undergo autoxidation reactions that tend to form low-volatility products and significantly contribute to alkane SOA formation. To account for this, an autoxidation reduction factor, as a function of the degree and position of branching, was applied to the lumped groups that contain autoxidation products. The resulting product distributions were then applied to the UNIPAR model for predicting branched-alkane SOA formation. The simulated SOA mass was compared to SOA data generated under varying experimental conditions (i.e., NOx levels, seed conditions, and humidity) in an outdoor photochemical smog chamber. Branched-alkane SOA yields were significantly impacted by NOx levels but insignificantly impacted by seed conditions or humidity. The SOA formation from branched and linear alkanes in diesel fuel was simulated to understand the relative importance of branched and linear alkanes with a wide range of carbon numbers. Overall, branched alkanes accounted for a higher proportion of SOA mass than linear alkanes due to their higher contribution to diesel fuel.</p

Spheroidization Heat Treatment Conditions with Data Analysis in Medium Carbon Cr-Mo Steel for Ultra High Strength Cold Heading

The degree to which parameters affect the spheroidization heat treatment of steel was calculated by setting the spheroidization heat treatment conditions of Cr-Mo steel and using data analysis such as S/N ratio and ANOVA. After analyzing the transformation temperatures of the steel, Ac1 and Ac3, using a DSC, the conditions were set accordingly. The surface hardness was measured for the conditions and used as an evaluation index. The correlation was analyzed by comparing the spheroidized volume fraction and the surface hardness, and the Pearson correlation coefficient was -0.88, proving that a correlation existed between the two values. Using S/N ratio and ANOVA, the degree to which each control parameter affects the decrease in the surface hardness was analyzed, qualitatively and quantitatively. For the S/N ratio, priority affecting the surface hardness for each control parameter was analyzed. The 1st heating temperature was found to have a more preferential effect on the surface hardness than the 1st heating time and the 2nd heating temperature. Using ANOVA, the 1st heating temperature was determined to be a very significant factor with the greatest influence, contributing 73.2% to the surface hardness. Intercritical annealing is a suitable spheroidization heat treatment condition, so if the surface hardness of the steel needs to be reduced using Intercritical annealing, the 1st heating temperature and time should be designed as the priority

Prompt Injection: Parameterization of Fixed Inputs

Recent works have shown that attaching prompts to the input is effective at conditioning Language Models (LM) to perform specific tasks. However, prompts are always included in the input text during inference, thus incurring substantial computational and memory overhead. Also, there is currently no straightforward method of utilizing prompts that are longer than the maximum input length of the LMs without incurring additional costs during inference. We propose Prompt Injection (PI), a novel formulation of injecting the prompt into the parameters of an LM to be an efficient alternative to attaching fixed prompts to the input. We show that in scenarios with long fixed prompts, PI can be up to 280 times more efficient in terms of total FLOPs than previous approaches. We further explore methodologies for PI and show promising results in persona-dependent conversation, semantic parsing, and zero-shot learning with task instructions. Through these explorations, we show that PI can be a promising direction for conditioning language models, especially in scenarios with long and fixed prompts.Comment: PING results in Table 2 updated (bug fixed

Automatic Construction of a Korean Toxic Instruction Dataset for Ethical Tuning of Large Language Models

Caution: this paper may include material that could be offensive or distressing. The advent of Large Language Models (LLMs) necessitates the development of training approaches that mitigate the generation of unethical language and aptly manage toxic user queries. Given the challenges related to human labor and the scarcity of data, we present KoTox, comprising 39K unethical instruction-output pairs. This collection of automatically generated toxic instructions refines the training of LLMs and establishes a foundational framework for improving LLMs' ethical awareness and response to various toxic inputs, promoting more secure and responsible interactions in Natural Language Processing (NLP) applications.Comment: NeurIPS 2023 Workshop on Instruction Tuning and Instruction Followin

Generalized Guidance Scheme for Low-Thrust Orbit Transfer

The authors present an orbital guidance scheme for the satellite with an electrical propulsion system using a Lyapunov feedback control. The construction of a Lyapunov candidate is based on orbital elements, which consist of angular momentum and eccentricity vectors. This approach performs orbit transfers between any two arbitrary elliptic or circular orbits without any singularity issues. These orbital elements uniquely describe a non degenerate Keplerian orbit. The authors improve the reliability of the existing Lyapunov orbital guidance scheme by considering the energy term. Additional improvement is achieved by adding the penalty function. Furthermore, it is shown that the final suggested approach is suitable for the satellite passing the earth’s shadow area