Excessive cargo sway during crane operations in the current shipbuilding industry is a major problem that causes safety accidents and work delays. Therefore, the development of stable crane control technology is essential. In this study, a crane control algorithm that simultaneously achieves accurate movement to target positions and sway minimization was developed using reinforcement learning. In dynamics modeling, the Discrete Euler-Lagrange Equation was applied to significantly reduce the computational complexity of existing methods, and the Proximal Policy Optimization (PPO) method was used for control policy learning. A three-dimensional virtual environment was constructed to perform learning under various travel distances and operating conditions, and the performance of the developed algorithm was compared and verified against the traditional trapezoidal velocity profile. Experimental results showed that the proposed method exhibited significant improvements in position control precision and sway suppression performance compared to existing methods. The results of this study are expected to contribute to the implementation of automated crane control systems in actual shipyard environments.
LU Zhan, WANG Jian, MA Qingyan, XU Changjian, LIANG Xiaofeng
Thrust allocation serves as a critical means for achieving vector propulsion in unmanned surface vessels (USV) equipped with dual waterjet thrusters. However, existing thrust allocation methods employed in vessels featuring azimuth thrusters fail to address the resolution of vector forces for dual waterjet propulsion, due to characteristics such as thrust angle limitations and reverse thrust. To achieve vector motion control of a dual waterjet propelled USV, a hierarchical optimization-based thrust allocation algorithm is proposed. In the first tier, a vector synthesis approach incorporating enhanced angle constraints is utilized to acquire top-tier vector thrust satisfying constraints on the rotating range and rate characteristics of the thrusters. In the second tier, leveraging the top-tier vector thrust values as inputs and considering constraints on thruster power and power change frequency, an optimization method based on seeking minimal distance is proposed. This method facilitates the allocation of reverse thrust angles and nozzle flow velocities for waterjet thrusters, thereby resolving singular issues in dual waterjet thrust allocation. Simulation experiments and the semi-physical simulation experiments validate the effectiveness of the hierarchical optimization-based thrust allocation algorithm for dual waterjet thrusters. The results indicate that this method enables efficient thrust allocation for dual waterjet thrusters, while concurrently limiting fluctuations in thruster power frequency and amplitude during expected thrust variations, thereby reducing shafting wear while achieving target vector thrust.
Engineering (General). Civil engineering (General), Chemical engineering
Massimiliano Di Penta, Kelly Blincoe, Marsha Chechik
et al.
As software engineering conferences grow in size, rising costs and outdated formats are creating barriers to participation for many researchers. These barriers threaten the inclusivity and global diversity that have contributed to the success of the SE community. Based on survey data, we identify concrete actions the ACM Special Interest Group on Software Engineering (SIGSOFT) can take to address these challenges, including improving transparency around conference funding, experimenting with hybrid poster presentations, and expanding outreach to underrepresented regions. By implementing these changes, SIGSOFT can help ensure the software engineering community remains accessible and welcoming.
The proliferation of AI-powered search engines has shifted information discovery from traditional link-based retrieval to direct answer generation with selective source citation, creating new challenges for content visibility. While existing Generative Engine Optimization (GEO) approaches focus primarily on semantic content modification, the role of structural features in influencing citation behavior remains underexplored. In this paper, we propose GEO-SFE, a systematic framework for structural feature engineering in generative engine optimization. Our approach decomposes content structure into three hierarchical levels: macro-structure (document architecture), meso-structure (information chunking), and micro-structure (visual emphasis), and models their impact on citation probability across different generative engine architectures. We develop architecture-aware optimization strategies and predictive models that preserve semantic integrity while improving structural effectiveness. Experimental evaluation across six mainstream generative engines demonstrates consistent improvements in citation rate (17.3 percent) and subjective quality (18.5 percent), validating the effectiveness and generalizability of the proposed framework. This work establishes structural optimization as a foundational component of GEO, providing a data-driven methodology for enhancing content visibility in LLM-powered information ecosystems.
Ana B. M. Bett, Thais S. Nepomuceno, Edson OliveiraJr
et al.
Context: The empirical software engineering (ESE) community has contributed to improving experimentation over the years. However, there is still a lack of rigor in describing controlled experiments, hindering reproducibility and transparency. Registered Reports (RR) have been discussed in the ESE community to address these issues. A RR registers a study's hypotheses, methods, and/or analyses before execution, involving peer review and potential acceptance before data collection. This helps mitigate problematic practices such as p-hacking, publication bias, and inappropriate post hoc analysis. Objective: This paper presents initial results toward establishing an RR template for Software Engineering controlled experiments using the Open Science Framework (OSF). Method: We analyzed templates of selected OSF RR types in light of documentation guidelines for controlled experiments. Results: The observed lack of rigor motivated our investigation of OSF-based RR types. Our analysis showed that, although one of the RR types aligned with many of the documentation suggestions contained in the guidelines, none of them covered the guidelines comprehensively. The study also highlights limitations in OSF RR template customization. Conclusion: Despite progress in ESE, planning and documenting experiments still lack rigor, compromising reproducibility. Adopting OSF-based RRs is proposed. However, no currently available RR type fully satisfies the guidelines. Establishing RR-specific guidelines for SE is deemed essential.
The increasingly severe issue of marine debris presents a critical threat to the sustainable development of marine ecosystems. Real-time detection is essential for timely intervention and cleanup. Furthermore, the density of marine debris exhibits significant depth-dependent variation, resulting in degraded detection accuracy. Based on 9625 publicly available underwater images spanning various depths, this study proposes UTNet, a lightweight neural model, to improve the effectiveness of real-time intelligent identification of marine debris through multidimensional optimization. Compared to Faster R-CNN, SSD, and YOLOv5/v8/v11/v12, the UTNet model demonstrates enhanced performance in random image detection, achieving maximum improvements of 3.5% in mAP50 and 9.3% in mAP50-95, while maintaining reduced parameter count and low computational complexity. The UTNet model is further evaluated on underwater videos for real-time debris recognition at varying depths to validate its capability. Results show that the UTNet model exhibits a consistently increasing trend in confidence levels across different depths as detection distance decreases, with peak values of 0.901 at the surface and 0.764 at deep-sea levels. In contrast, the other six models display greater performance fluctuations and fail to maintain detection stability, particularly at intermediate and deep depths, with evident false positives and missed detections. In summary, the lightweight UTNet model developed in this study achieves high detection accuracy and computational efficiency, enabling real-time, high-precision detection of marine debris at varying depths and ultimately benefiting mitigation and cleanup efforts.
<p><strong>Purpose:</strong> This study aims to examine the relationship between gender-responsive governance and conflict management practices, with a focus on how school leaders handle conflicts across various domains—competing, collaborating, compromising, avoiding, and accommodating. The analysis considers the key variables, including age, gender, length of service, and educational attainment. Based on the findings, a Gender-Responsive Governance and Conflict Management Practices Enhancement Training Program will be designed to address identified gaps and challenges effectively.</p><p><strong> </strong></p><p><strong>Methods:</strong> This study utilized a descriptive-correlational research design with a quantitative approach to examine gender-responsive governance and conflict management practices among selected Maritime Higher Education Institutions (MHEIs) in the Philippines. A total of 73 school leaders from various MHEIs participated in the study. Data were collected using two questionnaires: the first, based on the Gender Responsiveness Governance Tool developed by USAID, assessed the level of gender-responsive governance; the second, adapted from the Thomas-Kilmann Conflict Mode Instrument (TKI), evaluated the conflict management practices of the respondents. The data were analysed using statistical methods, including mean, standard deviation, t-test for independent samples, One-Way Analysis of Variance (ANOVA), and Pearson’s correlation coefficient (r).</p><p> </p><p><strong>Findings:</strong> Results revealed that respondent’ level of gender-responsive governance according to age, gender, length of service, educational qualification, and management level was “high.” Generally, the topmost management practice of the respondents was collaborating except those respondents with shorter length of service (adopted compromising style); those with bachelor’s degree (adopted compromising style); and those with doctorate degree (adopted competing style). Statistically, no significant difference existed in the level of gender-responsive governance of the respondents when grouped and compared according to age, gender, length of service, educational qualification, and management level. Moreover, result showed a significant but weak to moderate degree of relationship between gender-responsive governance and conflict management practices of the respondents. In other words, respondents’ gender-responsive governance was, to some degree, reflective of their conflict management practices.</p><p><strong>Research Implications:</strong> The findings of this study underscore the importance of gender-responsive governance and its integration with conflict management practices in organizations. Despite no statistically significant differences based on demographic characteristics such as age, gender, length of service, educational qualification, and management level, the study highlights that a strong commitment to gender equality is a pervasive feature across diverse respondent groups. This suggests that gender-responsive governance principles are universally embraced by management, regardless of these variables. Besides, finding implies that fostering a gender-responsive environment may enhance the effectiveness of conflict resolution strategies, as inclusive decision-making processes that prioritize collaboration and fairness are more likely to lead to positive outcomes. Future research could explore deeper into how various conflict management styles, particularly the collaborating style, influence gender equality and fairness in the workplace. Additionally, further investigation could examine how specific demographic factors—such as length of service or educational qualifications—may influence the adoption of particular conflict management styles, even when their effect on gender-responsive governance appears neutral.</p><p><strong>Practical Implications:</strong> Practically, the findings suggest that organizations can benefit from promoting gender-responsive governance practices across all management levels, as these practices contribute to creating an inclusive, fair, and equitable workplace. The strong preference for collaborative conflict management among respondents indicates that encouraging open, inclusive dialogue can resolve workplace conflicts more effectively, which is particularly important in diverse work environments where gender equality is a priority. Organizations could consider training programs that emphasize both gender-responsive governance and collaborative conflict resolution techniques. Such programs would help leaders develop the skills necessary to address conflicts with a mindset that prioritizes fairness and inclusivity, benefiting both men and women in the workplace. Furthermore, even though no significant differences were observed in relation to respondents' personal characteristics, understanding how demographic factors might influence management practices could assist in tailoring strategies to address potential barriers to achieving gender equality and conflict resolution in the workplace. Incorporating these insights into organizational practices will not only foster better conflict management but also enhance overall workplace harmony, leading to improved employee satisfaction and retention.</p><p> </p><p><strong>Received on: 28 November 2024 </strong></p><p><strong>Accepted on: 22 February 2025 </strong></p><p><strong>Published on: 13 March 2025</strong></p>
Ship Shoal, a large transgressive sand body on the Louisiana continental shelf, is a critical sediment source for coastal restoration. This study evaluates spatial and temporal variability in sediment grain size, percents organic matter (%OM), and carbonate (%CO<sub>3</sub>) across the shoal crest (REF), Caminada Dredge Pit (CAM), and Terrebonne Dredge Pit (TER). Sediment samples were collected between 2020 and 2022 using box cores and analyzed for grain size, %OM, and %CO<sub>3</sub>, with temporal and spatial patterns assessed through statistical comparisons, correlation analyses, and random forest regression models. Results show that dredged areas act as sinks for fine-grained, organic-rich sediments, with CAM consistently exhibiting the smallest median grain sizes and highest %OM, while REF maintained coarse, well-sorted sands. Carbonate enrichment reflected long-term depositional regimes, with REF exhibiting the highest %CO<sub>3</sub> due to the absence of dredging disturbance. Grain size and %CO<sub>3</sub> were identified as the strongest predictors of %OM, while %CO<sub>3</sub> was only weakly correlated with other sedimentary variables. Collectively, these findings demonstrate that dredge pits function as persistent repositories, with implications for benthic habitat resilience, sediment management, and coastal restoration planning. Future integration of hydrodynamic modeling with sediment transport and biogeochemical processes is needed to enhance predictive capability for managing dredged environments.
Jake Zappin, Trevor Stalnaker, Oscar Chaparro
et al.
This position paper examines the substantial divide between academia and industry within quantum software engineering. For example, while academic research related to debugging and testing predominantly focuses on a limited subset of primarily quantum-specific issues, industry practitioners face a broader range of practical concerns, including software integration, compatibility, and real-world implementation hurdles. This disconnect mainly arises due to academia's limited access to industry practices and the often confidential, competitive nature of quantum development in commercial settings. As a result, academic advancements often fail to translate into actionable tools and methodologies that meet industry needs. By analyzing discussions within quantum developer forums, we identify key gaps in focus and resource availability that hinder progress on both sides. We propose collaborative efforts aimed at developing practical tools, methodologies, and best practices to bridge this divide, enabling academia to address the application-driven needs of industry and fostering a more aligned, sustainable ecosystem for quantum software development.
Software development relies heavily on text-based communication, making sentiment analysis a valuable tool for understanding team dynamics and supporting trustworthy AI-driven analytics in requirements engineering. However, existing sentiment analysis tools often perform inconsistently across datasets from different platforms, due to variations in communication style and content. In this study, we analyze linguistic and statistical features of 10 developer communication datasets from five platforms and evaluate the performance of 14 sentiment analysis tools. Based on these results, we propose a mapping approach and questionnaire that recommends suitable sentiment analysis tools for new datasets, using their characteristic features as input. Our results show that dataset characteristics can be leveraged to improve tool selection, as platforms differ substantially in both linguistic and statistical properties. While transformer-based models such as SetFit and RoBERTa consistently achieve strong results, tool effectiveness remains context-dependent. Our approach supports researchers and practitioners in selecting trustworthy tools for sentiment analysis in software engineering, while highlighting the need for ongoing evaluation as communication contexts evolve.
UI automation is a useful technique for UI testing, bug reproduction, and robotic process automation. Recording user actions with an application assists rapid development of UI automation scripts, but existing recording techniques are intrusive, rely on OS or GUI framework accessibility support, or assume specific app implementations. Reverse engineering user actions from screencasts is non-intrusive, but a key reverse-engineering step is currently missing - recognizing human-understandable structured user actions ([command] [widget] [location]) from action screencasts. To fill the gap, we propose a deep learning-based computer vision model that can recognize 11 commands and 11 widgets, and generate location phrases from action screencasts, through joint learning and multi-task learning. We label a large dataset with 7260 video-action pairs, which record user interactions with Word, Zoom, Firefox, Photoshop, and Windows 10 Settings. Through extensive experiments, we confirm the effectiveness and generality of our model, and demonstrate the usefulness of a screencast-to-action-script tool built upon our model for bug reproduction.
Jonathan Ullrich, Matthias Koch, Andreas Vogelsang
With the advent of generative LLMs and their advanced code generation capabilities, some people already envision the end of traditional software engineering, as LLMs may be able to produce high-quality code based solely on the requirements a domain expert feeds into the system. The feasibility of this vision can be assessed by understanding how developers currently incorporate requirements when using LLMs for code generation-a topic that remains largely unexplored. We interviewed 18 practitioners from 14 companies to understand how they (re)use information from requirements and other design artifacts to feed LLMs when generating code. Based on our findings, we propose a theory that explains the processes developers employ and the artifacts they rely on. Our theory suggests that requirements, as typically documented, are too abstract for direct input into LLMs. Instead, they must first be manually decomposed into programming tasks, which are then enriched with design decisions and architectural constraints before being used in prompts. Our study highlights that fundamental RE work is still necessary when LLMs are used to generate code. Our theory is important for contextualizing scientific approaches to automating requirements-centric SE tasks.
Alexandra Mazak-Huemer, Christian Huemer, Michael Vierhauser
et al.
With the increasing significance of Research, Technology, and Innovation (RTI) policies in recent years, the demand for detailed information about the performance of these sectors has surged. Many of the current tools are limited in their application purpose. To address these issues, we introduce a requirements engineering process to identify stakeholders and elicitate requirements to derive a system architecture, for a web-based interactive and open-access RTI system monitoring tool. Based on several core modules, we introduce a multi-tier software architecture of how such a tool is generally implemented from the perspective of software engineers. A cornerstone of this architecture is the user-facing dashboard module. We describe in detail the requirements for this module and additionally illustrate these requirements with the real example of the Austrian RTI Monitor.