In this study, we explore the potential of using quantum natural language processing (QNLP) to inverse design metal-organic frameworks (MOFs) with targeted properties. Specifically, by analyzing 150 hypothetical MOF structures consisting of 10 metal nodes and 15 organic ligands, we categorize these structures into four distinct classes for pore volume and $H_{2}$ uptake values. We then compare various QNLP models (i.e. the bag-of-words, DisCoCat (Distributional Compositional Categorical), and sequence-based models) to identify the most effective approach to process the MOF dataset. Using a classical simulator provided by the IBM Qiskit, the bag-of-words model is identified to be the optimum model, achieving validation accuracies of 85.7% and 86.7% for binary classification tasks on pore volume and $H_{2}$ uptake, respectively. Further, we developed multi-class classification models tailored to the probabilistic nature of quantum circuits, with average test accuracies of 88.4% and 80.7% across different classes for pore volume and $H_{2}$ uptake datasets. Finally, the performance of generating MOF with target properties showed accuracies of 93.5% for pore volume and 89% for $H_{2}$ uptake, respectively. Although our investigation covers only a fraction of the vast MOF search space, it marks a promising first step towards using quantum computing for materials design, offering a new perspective through which to explore the complex landscape of MOFs.

In the rapidly evolving field of business process management, there is a growing need for analytical tools that can transform complex data into actionable insights. This research introduces a novel approach by integrating Large Language Models (LLMs), such as ChatGPT, into process mining tools, making process analytics more accessible to a wider audience. The study aims to investigate how ChatGPT enhances analytical capabilities, improves user experience, increases accessibility, and optimizes the architectural frameworks of process mining tools. The key innovation of this research lies in developing a tailored prompt engineering strategy for each process mining submodule, ensuring that the AI-generated outputs are accurate and relevant to the context. The integration architecture follows an Extract, Transform, Load (ETL) process, which includes various process mining engine modules and utilizes zero-shot and optimized prompt engineering techniques. ChatGPT is connected via APIs and receives structured outputs from the process mining modules, enabling conversational interactions. To validate the effectiveness of this approach, the researchers used data from 17 companies that employ BehfaLab's Process Mining Tool. The results showed significant improvements in user experience, with an expert panel rating 72% of the results as "Good". This research contributes to the advancement of business process analysis methodologies by combining process mining with artificial intelligence. Future research directions include further optimization of prompt engineering, exploration of integration with other AI technologies, and assessment of scalability across various business environments. This study paves the way for continuous innovation at the intersection of process mining and artificial intelligence, promising to revolutionize the way businesses analyze and optimize their processes.

To advance the circular economy (CE), it is crucial to gain insights into the evolution of public sentiments, cognitive pathways of the masses concerning circular products and digital technology, and recognise the primary concerns. To achieve this, we collected data related to the CE from diverse platforms including Twitter, Reddit, and The Guardian. This comprehensive data collection spanned across three distinct strata of the public: the general public, professionals, and official sources. Subsequently, we utilised three topic models on the collected data. Topic modelling represents a type of data-driven and machine learning approach for text mining, capable of automatically categorising a large number of documents into distinct semantic groups. Simultaneously, these groups are described by topics, and these topics can aid in understanding the semantic content of documents at a high level. However, the performance of topic modelling may vary depending on different hyperparameter values. Therefore, in this study, we proposed a framework for topic modelling with hyperparameter optimisation for CE and conducted a series of systematic experiments to ensure that topic models are set with appropriate hyperparameters and to gain insights into the correlations between the CE and public opinion based on well-established models. The results of this study indicate that concerns about sustainability and economic impact persist across all three datasets. Official sources demonstrate a higher level of engagement with the application and regulation of CE. To the best of our knowledge, this study is pioneering in investigating various levels of public opinions concerning CE through topic modelling with the exploration of hyperparameter optimisation.

Ashland City, Tennessee, located within the Lower Cumberland Sycamore watershed, is highly susceptible to flooding due to increased upstream water levels. This study aimed to develop a robust flood prediction model for the city, utilizing water level data at 30-minute intervals from ten USGS gauge stations within the watershed. A Gated Recurrent Unit (GRU) network, known for its ability to effectively process sequential time-series data, was used. The model was trained, validated, and tested using a year-long dataset (January 2021-January 2022), and its performance was evaluated using statistical metrics including Nash-Sutcliffe Efficiency (NSE), Root Mean Squared Error (RMSE), Percent Bias (PBIAS), Mean Absolute Error (MAE), and Coefficient of Determination (R^2). The results demonstrated a high level of accuracy, with the model explaining 98.2% of the variance in the data. Despite minor discrepancies between predicted and observed values, the GRU model proved to be an effective tool for flood prediction in Ashland City, with potential applications for enhancing disaster preparedness and response efforts in Ashland City.

With the proliferation of screening tools for chemical testing, it is now possible to create vast databases of chemicals easily. However, rigorous statistical methodologies employed to analyse these databases are in their infancy, and further development to facilitate chemical discovery is imperative. In this paper, we present conditional Gaussian process models to predict ordinal outcomes from chemical experiments, where the inputs are chemical compounds. We implement the Tanimoto distance, a metric on the chemical space, within the covariance of the Gaussian processes to capture correlated effects in the chemical space. A novel aspect of our model is that the kernel contains a scaling parameter, a feature not previously examined in the literature, that controls the strength of the correlation between elements of the chemical space. Using molecular fingerprints, a numerical representation of a compound's location within the chemical space, we show that accounting for correlation amongst chemical compounds improves predictive performance over the uncorrelated model, where effects are assumed to be independent. Moreover, we present a genetic algorithm for the facilitation of chemical discovery and identification of important features to the compound's efficacy. A simulation study is conducted to demonstrate the suitability of the proposed methods. Our proposed methods are demonstrated on a hazard classification problem of organic solvents.

Recently, a noticeable trend has emerged in developing pre-trained foundation models in the domains of CV and NLP. However, for molecular pre-training, there lacks a universal model capable of effectively applying to various categories of molecular tasks, since existing prevalent pre-training methods exhibit effectiveness for specific types of downstream tasks. Furthermore, the lack of profound understanding of existing pre-training methods, including 2D graph masking, 2D-3D contrastive learning, and 3D denoising, hampers the advancement of molecular foundation models. In this work, we provide a unified comprehension of existing pre-training methods through the lens of contrastive learning. Thus their distinctions lie in clustering different views of molecules, which is shown beneficial to specific downstream tasks. To achieve a complete and general-purpose molecular representation, we propose a novel pre-training framework, named UniCorn, that inherits the merits of the three methods, depicting molecular views in three different levels. SOTA performance across quantum, physicochemical, and biological tasks, along with comprehensive ablation study, validate the universality and effectiveness of UniCorn.

This article surveys the growing interest in utilizing Deep Learning (DL) as a powerful tool to address challenging problems in earthquake engineering. Despite decades of advancement in domain knowledge, issues such as uncertainty in earthquake occurrence, unpredictable seismic loads, nonlinear structural responses, and community engagement remain difficult to tackle using domain-specific methods. DL offers promising solutions by leveraging its data-driven capacity for nonlinear mapping, sequential data modeling, automatic feature extraction, dimensionality reduction, optimal decision-making, etc. However, the literature lacks a comprehensive review that systematically covers a consistent scope intersecting DL and earthquake engineering. To bridge the gap, the article first discusses methodological advances to elucidate various applicable DL techniques, such as multi-layer perceptron (MLP), convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), autoencoder (AE), transfer learning (TL), reinforcement learning (RL), and graph neural network (GNN). A thorough research landscape is then disclosed by exploring various DL applications across different research topics, including vision-based seismic damage assessment and structural characterization, seismic demand and damage state prediction, seismic response history prediction, regional seismic risk assessment and community resilience, ground motion (GM) for engineering use, seismic response control, and the inverse problem of system/damage identification. Suitable DL techniques for each research topic are identified, emphasizing the preeminence of CNN for vision-based tasks, RNN for sequential data, RL for community resilience, and unsupervised learning for GM analysis. The article also discusses opportunities and challenges for leveraging DL in earthquake engineering research and practice, highlighting the need for open-access multimodal big data and efforts to enhance model interpretability and incorporate physics information into DL. Finally, the paper advocates for DL applications to further advance the research frontier of uncertainty quantification in performance-based earthquake engineering.

Machine learning has recently emerged as a powerful tool for generating new molecular and material structures. The success of state-of-the-art models stems from their ability to incorporate physical symmetries, such as translation, rotation, and periodicity. Here, we present a novel generative method called Response Matching (RM), which leverages the fact that each stable material or molecule exists at the minimum of its potential energy surface. Consequently, any perturbation induces a response in energy and stress, driving the structure back to equilibrium. Matching to such response is closely related to score matching in diffusion models. By employing the combination of a machine learning interatomic potential and random structure search as the denoising model, RM exploits the locality of atomic interactions, and inherently respects permutation, translation, rotation, and periodic invariances. RM is the first model to handle both molecules and bulk materials under the same framework. We demonstrate the efficiency and generalization of RM across three systems: a small organic molecular dataset, stable crystals from the Materials Project, and one-shot learning on a single diamond configuration.

The current safeguard mechanisms for large language models (LLMs) are indeed susceptible to jailbreak attacks, making them inherently fragile. Even the process of fine-tuning on apparently benign data for downstream tasks can jeopardize safety. One potential solution is to conduct safety fine-tuning subsequent to downstream fine-tuning. However, there's a risk of catastrophic forgetting during safety fine-tuning, where LLMs may regain safety measures but lose the task-specific knowledge acquired during downstream fine-tuning. In this paper, we introduce a safety realignment framework through subspace-oriented model fusion (SOMF), aiming to combine the safeguard capabilities of initially aligned model and the current fine-tuned model into a realigned model. Our approach begins by disentangling all task vectors from the weights of each fine-tuned model. We then identify safety-related regions within these vectors by subspace masking techniques. Finally, we explore the fusion of the initial safely aligned LLM with all task vectors based on the identified safety subspace. We validate that our safety realignment framework satisfies the safety requirements of a single fine-tuned model as well as multiple models during their fusion. Our findings confirm that SOMF preserves safety without notably compromising performance on downstream tasks, including instruction following in Chinese, English, and Hindi, as well as problem-solving capabilities in Code and Math.

Laser powder bed fusion (LPBF) has shown promise for wide range of applications due to its ability to fabricate freeform geometries and generate a controlled microstructure. However, components generated by LPBF still possess sub-optimal mechanical properties due to the defects that are created during laser-material interactions. In this work, we investigate mechanism of spatter formation, using a high-fidelity modelling tool that was built to simulate the multi-physics phenomena in LPBF. The modelling tool have the capability to capture the 3D resolution of the meltpool and the spatter behavior. To understand spatter behavior and formation, we reveal its properties at ejection and evaluate its variation from the meltpool, the source where it is formed. The dataset of the spatter and the meltpool collected consist of 50 % spatter and 50 % melt pool samples, with features that include position components, velocity components, velocity magnitude, temperature, density and pressure. The relationship between the spatter and the meltpool were evaluated via correlation analysis and machine learning (ML) algorithms for classification tasks. Upon screening different ML algorithms on the dataset, a high accuracy was observed for all the ML models, with ExtraTrees having the highest at 96 % and KNN having the lowest at 94 %.