Modeling engines using physics-based approaches is a traditional and widely-accepted method for predicting in-cylinder pressure and the start of combustion (SOC). However, developing such intricate models typically demands significant effort, time, and knowledge about the underlying physical processes. In contrast, machine learning techniques have demonstrated their potential for building models that are not only rapidly developed but also efficient. In this study, we employ a machine learning approach to predict the cylinder pressure of a homogeneous charge compression ignition (HCCI) engine. We utilize a long short-term memory (LSTM) based machine learning model and compare its performance against a fully connected neural network model, which has been employed in previous research. The LSTM model’s results are evaluated against experimental data, yielding a mean absolute error of 0.37 and a mean squared error of 0.20. The cylinder pressure prediction is presented as a time series, expanding upon prior work that focused on predicting pressure at discrete points in time. Our findings indicate that the LSTM method can accurately predict the cylinder pressure of HCCI engines up to 256 time steps ahead.

Robotic mobile fulfillment systems have received increased attention in high throughput supply chain solutions. These systems are complex and require understanding of the single components and their interaction. Components include picking, replenishing, stock keeping unit allocation and task allocation. This paper focuses on the component of stock keeping unit allocation which is an NP-Hard combinatorial optimization problem and belongs to the cutting & packing problems. The problem is defined via a mathematical model which is the first bin packing model for pods with compartments in RMFS. The resulting mixed integer programming model is then solved for small instances via Gurobi, a state-of-the-art solver for mathematical programming. The model is verified with a small-scale example. To obtain a solution for larger instances, we develop a simulated annealing approach and compare it to the exact model. Different operators are compared and evaluated on heterogeneous toy-examples of different size. Finally, a packing case with stock keeping units and pods derived from an industrial case is explored, which demonstrated the application of our packing algorithm to large instances in the industrial environment.

E-participation platforms, such as iVoting and Join in Taiwan, provide digital spaces for citizens to engage in deliberation, voting, and oversight. As a forerunner in Asia, Taiwan has implemented these platforms to enhance participatory democracy. However, there is still limited research on the specific content debated on these platforms. Utilising recent advancements in Natural Language Processing, the content of proposals that users have submitted between 2015 and 2025 is explored. In this study, a pipeline for mining text corpora scraped from these platforms in the context of political analysis is proposed. The pipeline is applied to two datasets which have different characteristics. A topic model for each of the two platforms is generated and later evaluated with OCTIS (Optimizing and Comparing Topic Models Is Simple) and compared to different baselines. Our research highlights the trade-offs between model performance and processing time, emphasizing the balance between accuracy and meaningful topic creation. By integrating a translation pipeline from Chinese to English within the text-mining process, our method also demonstrates a solid approach to overcome language barriers. Consequently, our method is adaptable to e-participation platforms in various languages, providing decision-makers with a more comprehensive tool to understand citizens’ needs and enabling the formulation of more informed and effective policies.