Corporate Social Responsibility (CSR) reporting has become an indispensable mechanism for organizations to communicate their sustainability initiatives. However, the growing volume and complexity of these reports necessitates the integration of Natural Language Processing (NLP)-driven text mining techniques to enhance transparency, comparability, and strategic decision-making. This study employs NLP and text mining methodologies to systematically analyze CSR reports with emphasis on environmental sustainability from major public listed companies in Taiwan. Utilizing Principal Component Analysis (PCA), this study classifies sustainability-related topics, extracts key Sustainable Development Goals (SDG)-aligned terms, and evaluates the textual similarities between CSR reports and SDG targets. Five SDGs encompassing 39 specific targets form the analytical framework, and 225 feature words are identified through text mining. The findings indicate that (1) automated CSR topic classification in Chinese is viable, though expert validation remains crucial for linguistic accuracy and semantic integrity; (2) SDG feature word distribution follows the Pareto Principle, with 10% of words contributing to 50.4%, and 28.4% accounting for 80% of total TF-IDF weights; (3) CSR reporting varies by industry, with financial holdings emphasizing sustainable management, energy supply, and water efficiency, while the electronics sector prioritizes waste reduction, recycling, and product lifecycle management; (4) PCA-based classification effectively aligns CSR reports with SDG targets, with textual similarity analysis proving more accurate than principal component scores. From a strategic business perspective, these findings offer critical insights for corporate leaders seeking to refine their sustainability strategies. NLP-based CSR analysis enables companies to benchmark their Environmental, Social, and Governance (ESG) performance against industry peers, identify sustainability gaps, and align corporate strategies with evolving regulatory landscapes and stakeholder expectations. Financial institutions can leverage these insights to develop sustainable finance mechanisms, while manufacturers can enhance circular economy practices by optimizing resource efficiency and waste management. Moreover, policymakers and investors can utilize NLP-driven text mining techniques to assess corporate sustainability efforts more systematically, ensuring greater accountability and fostering data-driven decision-making in ESG governance.

This study presents a soft robotic gripper for grasping irregular objects. The optimal design is based on the proposed topology optimization and size optimization methods with the objective to maximize the mechanical advantage (MA, which is defined as the ratio of output force to the input force) of the analyzed compliant mechanism. The optimal design is prototyped using silicon rubber material. Experimental tests including MA test, geometric advantage (GA, which is defined as the ratio of output displacement to the input displacement) test, adaptability test, and grasping test are carried out to investigate the design. A performance index has also been proposed to evaluate the grasping performance of the grippers. The results show the developed gripper is with the highest performance index, which represents the developed gripper is with better adaptability, faster response, higher payload and stability in overall.

This paper presents a roller-based peel-off demolding method and system for demolding of the polydimethylsiloxane (PDMS) micro pillars with high aspect ratio (6:1). The method can reduce the maximum stress exerted on the microstructures during the step of separating the PDMS film from the mold. Therefore the yield rate can be greatly increased. In this paper, the peel-off demolding process has been analyzed, and the major failure mode of the process is identified as the lateral collapse of the micro pillars. The effects of several process parameters (such as cycle time, sample thickness and temperature) on yield rate are identified. A lower demolding speed, larger thickness and lower temperature of the sample can yield better results. A prototype of the automated demolding system has been developed. The experimental results show that the system can achieve stable results with 99% yield rate.