National Kaohsiung First University of Science and Technology

This study investigates the sequential integration of hydrothermal liquefaction (HTL) and anaerobic digestion (AD) as a strategy for resource recovery from municipal wastewater (MW) and semiconductor packaging wastewater (SPW) sludges. The primary objective is to determine the influence of HTL pretreatment on conversion efficiencies, water quality metrics, and subsequent anaerobic biodegradability. Specifically, the research focuses on biogas generation, COD removal, and the potential to promote circular resource utilization. HTL was conducted under controlled temperature (150–374 °C) and pressure (10–25 MPa) conditions, followed by batch AD at 41 °C using hydrogen- and methane-producing inocula at various ratios, specifically 20%, 50%, and 80%. Key variables, including total solids (TS), suspended solids (SS), chemical oxygen demand (COD), pH, and electrical conductivity (EC), were monitored to assess degradation efficiency and resource recovery. Additionally, chemical modifications in HTL-processed sludge were characterized using Fourier Transform Infrared Spectroscopy (FTIR). Results indicate that MW sludge achieved significant reductions in TS (65.3%) and enhanced biogas production of 156.7 mL/g VS at 80% inoculum. These figures reflect high biodegradability and compatibility with AD. In contrast, SPW sludge demonstrated limited COD removal (26.6–85%) and lower biogas yields of 154.0 mL/g VS. These results are likely due to elevated salinity and compositional complexity. These findings suggest that while HTL pretreatment significantly improves MW sludge utilization, SPW sludge may require additional or alternative treatment strategies. Overall, this study clarifies key factors influencing the performance of integrated HTL-AD systems across distinct sludge types and lays a foundation for the further development of sustainable sludge management processes.

This study aims to evaluate the performance of artificial intelligence language models in the Industrial Engineer Certification Examination ‎and to analyze their accuracy across different subjects. A total of 750 past exam questions (from 2019 to 2024) across five subjects, namely ‎Quality Management, Production and Operations Management, Operations Research, Engineering Economics, and Human Factors ‎Engineering, were tested using ChatGPT-3.5 and ChatGPT-4o. The models’ responses were compared with standard answers to compute ‎accuracy rates and identify performance differences. Additionally, the Chain of Thought (CoT) and Tree of Thought (ToT) reasoning ‎frameworks were applied to incorrect responses from GPT-4o to assess improvements in reasoning. The results show that ChatGPT-4o ‎achieved significantly higher accuracy and reasoning coherence than ChatGPT-3.5, particularly in Quality Management and Production ‎Management. Overall, this study demonstrates the potential of generative AI as an effective tool for professional education and exam ‎preparation, offering insights for future model optimization and educational integration‎.

Purpose This study aims to expand the theory of planned behavior (TPB) by incorporating place attachment as a mediating variable and word-of-mouth (WOM) as a moderating variable and utilizing a mediation–moderation analytical framework to explore tourists’ behavioral intention in marine leisure contexts. Design/methodology/approach This study used anonymous questionnaires and face-to-face data collection as the main survey methods and investigated water sports and marine leisure activities to explore visitors’ views when they participated in marine recreational activities. First, we used JASP to analyze the confirmatory factor analyses with all measures to determine whether the hypothesized construct model fit the data well. Second, structural equation modeling was utilized to explore the interactions of the latent variables in relation to the mediating effects. Findings This research identifies and confirms the important mediating role of place attachment and destination image in the TPB model. Additionally, considering the prevalence of internet technology, this study introduces word-of-mouth (WOM) as a moderating effect, confirming its important reinforcing function in place attachment, destination image and behavioral intention. Practical implications Practically, tourism managers should design experiential interventions – such as storytelling tours, marine heritage events or ecologically themed festivals – that cultivate emotional connection (place attachment) while reinforcing positive destination imagery. This alignment of emotional branding with tourists’ behavioral triggers informed by the TPB offers a strategic roadmap for increasing loyalty, revisit intention and social sharing behaviors, especially through WOM amplification. Originality/value The findings confirm and extend the TPB framework by demonstrating that emotional constructs, specifically place attachment, mediate the influence of TPB variables (attitudes, subjective norms and perceived behavioral control) on behavioral intention. The findings suggest that rational decision-making models can be enhanced by incorporating affective bonds to better predict behavior, aligning with recent theoretical advancements that emphasize integrated emotion–cognition models.

Based on job postings from the "51job" platform in March 2025, this study employs content analysis to conduct a assessment of 866 valid positions in the industrial robotics industry across nine mainland cities in the Guangdong-Hong Kong-Macao Greater Bay Area. The aim is to examine the structural characteristics and inherent contradictions of talent demand in this industry. The findings are as follows: First, talent demand exhibits significant geographical agglomeration and functional division, with Shenzhen, Guangzhou, and Dongguan forming a synergistic chain of "R&Dtransformation-application." Second, private small and medium-sized enterprises constitute the primary source of labor demand (accounting for over 80%); however, their recruitment strategies demonstrate a strong preference for "immediate combat effectiveness," overly relying on work experience as a signal. This leads to an "experience paradox" and creates employment barriers for recent graduates. Third, the educational requirement structure for talent is pyramid-shaped, with over half of the positions demanding associate degrees (53.89%), while the demand for high-end R&D talent with master's degrees or higher is less than 2%, indicating that the industry currently focuses primarily on integration and application. Fourth, salaries are positively correlated with work experience, establishing a high entry barrier for early-career professionals.

Floating waste in lakes and harbors poses serious threats to aquatic ecosystems and local economies, while conventional manual cleaning remains inefficient and labor-intensive. This study develops an unmanned surface vehicle (USV) that integrates vision-based detection, a fuzzy logic-based PID controller, and multi-sensor fusion to enhance autonomous waste collection in calm waters. A prototype vessel was designed and tested in an indoor pool environment, where ultra-wideband (UWB) localization was used to emulate GPS positioning, a YOLO-based model performed real-time waste recognition, and landmark detection for docking assistance. The control system applied a Fuzzy-PID algorithm to regulate thrusters, using feedback from UWB, IMU, 2D LiDAR, and a depth camera for dynamic heading and speed adjustments. Experimental results demonstrate that the USV achieved stable autonomous navigation, obstacle avoidance, effective waste collection, and automatic docking. These findings validate the feasibility of the proposed approach and highlight its potential for practical applications in inland water waste management.