Green supplier evaluation and selection plays a crucial role in the green supply chain management of any organization to reduce the purchasing cost of materials and increase the flexibility and quality of products. An interval neutrosophic set (INS)—which is a generalization of fuzzy sets, intuitionistic fuzzy sets (IFS) and neutrosophic sets (NS)—can better handle the incomplete, indeterminate and inconsistent information than the other sets. This paper proposes a new integrated Quality Function Deployment (QFD) in support of the green supplier evaluation and selection process. In the proposed approach, INS is used to assess the relative importance of the characteristics that the purchased product should have (internal variables “WHATs”) in order to satisfy the company’s needs, the relevant supplier assessment criteria (external variables “HOWs”), the “HOWs”-“WHATs” correlation scores, the resulting weights of the “HOWs” and the impact of each potential supplier. The normalized weighted rating is then defined and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is developed to obtain a final ranking of green suppliers. A case study is applied to demonstrate the efficiency and computational procedure of the proposed method.

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.

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.