Structural Analysis by Modified Signature Matrix for Integro-differential-algebraic Equations

Integro-differential-algebraic equations (IDAE)s are widely used in applications of engineering and analysis. When there are hidden constraints in an IDAE, structural analysis is necessary. But if derivatives of dependent variables appear in their integrals, the existing definition of the signature matrix for an IDAE cannot be satisfied. Moreover, if an IDAE has a singular Jacobian matrix after structural analysis by the Sigma-method, improved structural analysis methods are proposed to regularize it. However, the optimal value of an IDAE may be negative which can not ensure the termination of the regularization. Furthermore, overestimation of the signature matrix may also lead to failure of its structural analysis. In this paper, firstly, we redefine the signature matrix and introduce a definition of the degree of freedom for IDAEs. Thus, the termination of improved structural analysis methods can be guaranteed. Secondly, the detection method by points is proposed to deal with the problem of overestimation of signature matrix. Thirdly, the embedding method has proved to suitable for structural unamenable IDAEs, including those types that arise from symbolic cancellation and numerical degeneration. Finally, the global numerical method is applied to an example of two-stage drive system which can help to find all solutions for IDAEs by witness points. Hopefully, through the example of pendulum curtain, the approach for IDAEs proposed in this paper can be applied to integro-partial-differential-algebraic equations (IPDAE)s.Comment: 33 pages, 4 figures, conferenc

Theoretical Investigation of the Biomass Conversion on Transition Metal Surfaces Based on Density Functional Theory Calculations and Machine Learning

During the past decades, heterogenous catalyzed conversion of biomass to hydrocarbons with similar or identical properties to conventional fossil fuels has gained significantly academic and industrial interest. However, the conventional heterogeneous catalysts such as sulfided NiMo/Al2O3 and CoMo/Al2O3 used have various drawbacks, such as short catalyst lifetime and high sulfur content of product. To overcome the limitations of the conventional sulfided catalysts, new catalysts must be developed, which requires a better understanding of the reaction mechanism of the biomass conversion. Based on density functional theory, in this thesis, we reported a computational calculation study of the reaction mechanism of the hydrodeoxygenation of propionic acid (our choice of model biomass molecules). For most of the times, however, biomass conversion is a very complicated process which usually have a very large reaction network, and thousands of intermediates and reaction steps are involved. Therefore, to theoretically identify the energy of those species by computational calculations is not realistic. We therefore also reported the potential of a combination of computational calculations and machine learning method to address the large reaction network of biomass conversion. Based on first principles calculations, a full microkinetic model have been developed for the vapor and liquid phase hydrodeoxygenation of propionic acid over a Pt (111) surface. Calculations suggest that decarboxylation does not occur at an appreciable rate. In the vapor phase, decarbonylation products, propionaldehyde and propanol are all produced at similar rates. However, in both liquid water and 1,4-dioxane, propanol and propionaldehyde are favored over decarbonylation products. While a condensed phase can shift the reaction rate and selectivity significantly, the dominant pathways towards the various products are hardly affected. Only for propionaldehyde production do we observe a shift in mechanism. A similar study was also conducted on the vapor and liquid phase hydrodeoxygenation of propionic acid on Rh (111) surface. Calculations suggest that both decarboxylation and decarbonylation do not occur at an appreciable rate in all reaction environments. Propanol and propionaldehyde are the main products and produced at similar rates in both vapor and liquid phases. Although a condensed phase can shift the reaction rate, the dominant pathways and selectivity towards the various products are hardly affected. In a combination of density functional theory calculations and machine learning method, we proposed a retraining cycle to predict the reaction mechanism of the hydrodeoxygenation of propionic acid on transition metal surfaces and the catalyst activity. With proper metal descriptors and species descriptors being used, our model predicts almost the same rate controlling species and reaction rates as that from models based on DFT calculations. We conclude that the approach we proposed can be readily used to address the complicated biomass conversion chemistry at a DFT accuracy without the need to do full DFT calculations for the large reaction network involved

A hybrid lattice Boltzmann and finite difference method for two-phase flows with soluble surfactants

A hybrid method is developed to simulate two-phase flows with soluble surfactants. In this method, the interface and bulk surfactant concentration equations of diffuse-interface form, which include source terms to consider surfactant adsorption and desorption dynamics, are solved in the entire fluid domain by the finite difference method, while two-phase flows are solved by a lattice Boltzmann color-gradient model, which can accurately simulate binary fluids with unequal densities. The flow and interface surfactant concentration fields are coupled by a modified Langmuir equation of state, which allows for surfactant concentration beyond critical micelle concentration. The capability and accuracy of the hybrid method are first validated by simulating three numerical examples, including the adsorption of bulk surfactants onto the interface of a stationary droplet, the droplet migration in a constant surfactant gradient, and the deformation of a surfactant-laden droplet in a simple shear flow, in which the numerical results are compared with theoretical solutions and available literature data. Then, the hybrid method is applied to simulate the buoyancy-driven bubble rise in a surfactant solution, in which the influence of surfactants is identified for varying wall confinement, Eotvos number and Biot number. It is found that surfactants exhibit a retardation effect on the bubble rise due to the Marangoni stress that resists interface motion, and the retardation effect weakens as the Eotvos or Biot number increases. We further show that the weakened retardation effect at higher Biot numbers is attributed to a decreased non-uniform effect of surfactants at the interface

A finite rotation, small strain 2D elastic head model, with applications in mild traumatic brain injury

Rotational head motions have been shown to play a key role in traumatic brain injury. There is great interest in developing methods to rapidly predict brain tissue strains and strain rates resulting from rotational head motions to estimate brain injury risk and to guide the design of protective equipment. Idealized continuum mechanics based head models provide an attractive approach for rapidly estimating brain strains and strain rates. These models are capable of capturing the wave dynamics and transient response of the brain while being significantly easier and faster to apply compared to more sophisticated and detailed finite element head models. In this work, we present a new idealized continuum mechanics based head model that accounts for the head's finite rotation, which is an improvement upon prior models that have been based on a small rotation assumption. Despite the simplicity of the model, we show that the proposed 2D elastic finite rotation head model predicts comparable strains to a more detailed finite element head model, demonstrating the potential usefulness of the model in rapidly estimating brain injury risk. This newly proposed model can serve as a basis for introducing finite rotations into more sophisticated head models in the future.Comment: 33pages, 11figure

Prompting and Evaluating Large Language Models for Proactive Dialogues: Clarification, Target-guided, and Non-collaboration

Conversational systems based on Large Language Models (LLMs), such as ChatGPT, show exceptional proficiency in context understanding and response generation. However, despite their impressive capabilities, they still possess limitations, such as providing randomly-guessed answers to ambiguous queries or failing to refuse users' requests, both of which are considered aspects of a conversational agent's proactivity. This raises the question of whether LLM-based conversational systems are equipped to handle proactive dialogue problems. In this work, we conduct a comprehensive analysis of LLM-based conversational systems, specifically focusing on three aspects of proactive dialogue systems: clarification, target-guided, and non-collaborative dialogues. To trigger the proactivity of LLMs, we propose the Proactive Chain-of-Thought prompting scheme, which augments LLMs with the goal planning capability over descriptive reasoning chains. Empirical findings are discussed to promote future studies on LLM-based proactive dialogue systems.Comment: Work in progres

Knowledge Distilled Ensemble Model for sEMG-based Silent Speech Interface

Voice disorders affect millions of people worldwide. Surface electromyography-based Silent Speech Interfaces (sEMG-based SSIs) have been explored as a potential solution for decades. However, previous works were limited by small vocabularies and manually extracted features from raw data. To address these limitations, we propose a lightweight deep learning knowledge-distilled ensemble model for sEMG-based SSI (KDE-SSI). Our model can classify a 26 NATO phonetic alphabets dataset with 3900 data samples, enabling the unambiguous generation of any English word through spelling. Extensive experiments validate the effectiveness of KDE-SSI, achieving a test accuracy of 85.9\%. Our findings also shed light on an end-to-end system for portable, practical equipment.Comment: 6 pages, 5 figure

Synthesis of graphene oxide–methacrylic acid–sodium allyl sulfonate copolymer and its tanning properties

AbstractGraphite oxide nanosheets (GONs) and the copolymer of GONs with methacrylic acid (MAA) and sodium allyl sulfonate (SAS) (poly(GON–MAA–SAS)) were prepared. The GONs in poly(GON–MAA–SAS) are smaller and uniformly dispersed, allowing them to penetrate into collagen fibers of leather and produce better tanning effects than current nano-tanning agents. Tanning effects due to chemical bonding and nanoeffects are elucidated by measuring the shrinkage temperature (Ts) of wet and dry leather. The results indicate that poly(GON–MAA–SAS) could be used alone as a tanning agent to provide excellent mechanical properties, especially good elasticity and softness, although the Ts is slightly lower than that of chrome-tanned leather. Poly(GON–MAA–SAS) in combination with a chrome tanning agent could allow the dosage of the latter to be halved. These results indicate the potential for new nano-tanning agents to reduce the pollution caused by tanning agents