For geotechnical structures with a strict control requirement of deformation, the high modulus and non-linear attenuation characteristics of the surrounding soil under small-strain conditions cannot be ignored during performance evaluation; the HSS constitutive model offers significant advantages over conventional approaches (e.g., Mohr–Coulomb) to describe the above soil behaviors. In this study, the theoretical framework of the HSS model, i.e., the yield function, hardening laws, and flow rule, is first elucidated. Subsequently, it is numerically implemented into the finite element software FssiCAS. The reliability of the FssiCAS software (Version 3.5) incorporating the HSS model is validated through a triaxial test and a physical test involving the horizontal loading of the monopile. Finally, taking the four-monopile jacket foundation of an offshore wind turbine (OWT) in Lianjiang County, China, as a representative, the HSS model is adopted to describe the mechanical behaviors of a seabed foundation. The horizontal bearing characteristics of the jacket foundation–seabed system under multi-angle horizontal loading are investigated, and the influence of the horizontal loading angle on the horizontal bearing capacity, jacket displacement, and seabed deformation is quantitatively elucidated. The results indicate that (1) the horizontal bearing capacity of the jacket is minimal when horizontal loading is along the diagonal of the four piles, representing the most severe loading case, and therefore, the horizontal bearing capacity of the jacket foundation–seabed system should be evaluated based on this case; and (2) the FE software FssiCAS has good reliability when dealing with pile–soil interaction problems involving complex geometries and complex mechanical behaviors of seabed soils. This study could provide technical support and an analysis platform for the design of jacket foundations for complex marine structures, such as OWTs.
Carbon credit systems have emerged as a policy tool to incentivize emission reductions and support the transition to clean energy. Reliable carbon-credit certification depends on mechanisms that connect actual, measured renewable-energy production to verifiable emission-reduction records. Although blockchain and IoT technologies have been applied to emission monitoring and trading, existing work offers limited support for certification processes, particularly for small and medium-scale renewable installations. This paper introduces a blockchain-based carbon-credit certification architecture, demonstrated through a 100 kWp photovoltaic case study, that integrates real-time IoT data collection, edge-level aggregation, and secure on-chain storage on a permissioned blockchain with smart contracts. Unlike approaches focused on trading mechanisms, the proposed system aligns with European legislation and voluntary carbon-market standards, clarifying the practical requirements and constraints that apply to photovoltaic operators. The resulting architecture provides a structured pathway for generating verifiable carbon-credit records and supporting third-party verification.
Alexandra González, Xavier Franch, Silverio Martínez-Fernández
Integrating Artificial Intelligence into Software Engineering (SE) requires having a curated collection of models suited to SE tasks. With millions of models hosted on Hugging Face (HF) and new ones continuously being created, it is infeasible to identify SE models without a dedicated catalogue. To address this gap, we present SEMODS: an SE-focused dataset of 3,427 models extracted from HF, combining automated collection with rigorous validation through manual annotation and large language model assistance. Our dataset links models to SE tasks and activities from the software development lifecycle, offering a standardized representation of their evaluation results, and supporting multiple applications such as data analysis, model discovery, benchmarking, and model adaptation.
Mairieli Wessel, Daniel Feitosa, Sangeeth Kochanthara
Rising publication pressure and the routine use of generative AI tools are reshaping how software engineering research is produced, assessed, and taught. While these developments promise efficiency, they also raise concerns about skill degradation, responsibility, and trust in scholarly outputs. This vision paper employs Design Fiction as a methodological lens to examine how such concerns might materialise if current practices persist. Drawing on themes reported in a recent community survey, we construct a speculative artifact situated in a near future research setting. The fiction is used as an analytical device rather than a forecast, enabling reflection on how automated assistance might impede domain knowledge competence, verification, and mentoring practices. By presenting an intentionally unsettling scenario, the paper invites discussion on how the software engineering research community in the future will define proficiency, allocate responsibility, and support learning.
The inclusion of internship courses in Software Engineering (SE) programs is essential for closing knowledge gaps and improving graduates' readiness for the software industry. Our study focuses on year-long internships at RMIT University (Melbourne, Australia), which offers in-depth industry engagement. We analysed how the course evolved over the last 10 years to incorporate students' needs and summarised the lessons learned that can be helpful for other educators supporting internship courses. Our qualitative analysis of internship data based on 91 reports during 2023-2024 identified three challenge themes the students faced, and which courses were found by students to be particularly beneficial during their internships. On this basis, we proposed recommendations for educators and companies to help interns overcome challenges and maximise their learning experience.
Kai-Ho Cheng, Chih-Hsun Chang, Yi-Chung Yang
et al.
The relationship between wind and waves has been extensively studied over time. However, understanding the local wind and wave relationship remains crucial for advancing renewable energy development and optimizing ocean management strategies. This study used wind and wave data collected by the ten weather buoys in the waters surrounding Taiwan to analyze regional sea states. The relationship between wind speed and significant wave height (SWH) was examined using regression analysis. Additionally, machine learning techniques were employed to assess the relative importance of features contributing to SWH growth. The regression analysis revealed that SWH in the waters surrounding Taiwan was not fully developed, with notable discrepancies observed between the waters east and west of Taiwan. According to the power law formula describing the relationship between wind speed and SWH, the eastern waters exhibited a larger prefactor coupled with a smaller scaling exponent, while the western waters manifested a converse parametric configuration. Through an evaluation of four machine learning algorithms, it was determined that wind speed is the most influential factor driving these regional differences, especially in the waters west of Taiwan. Beyond wind speed, air pressure or temperature emerged as the secondary feature factor governing wind–wave interactions in the waters east of Taiwan.
The design of ships has changed dramatically since the 1970s. We have shifted from manual drafting to digital tools and computers, mostly because computer technology has greatly improved. Nowadays, with the growth of smart digitalization in Industry 4.0, using modern digital software and tools makes ship design more efficient and enhances its quality throughout a ship's entire lifespan. However, this shift has also made operations more complex and requires users of the software to have more specialized training. Today, technologies like automated optimization, simulation-based design, managing the entire product lifecycle, digital twins, and artificial intelligence are commonly used in the shipping industry. These technologies are applied during both the design and construction phases, as well as in preparing and inspecting ships. This paper reviews major advances in these areas and discusses how the industry can address current and future challenges.
ZONG Haoxiang, ZHANG Chen, BAO Yanhong, WU Feng, CAI Xu
Aimed at the small-signal synchronization instability of grid-following (GFL) and grid-forming (GFM) converter system, a synchronization perspective frequency-domain modeling and analysis method is proposed, which can intuitively reveal mechanism and accurately judge multi-machine stability. Specifically, a node admittance matrix considering GFL, GFM converters, and the transmission network is established. Then, the frequency domain modal analysis (FMA) method is adopted to evaluate system instability characteristics. Afterwards, synchronization forward and feedback paths are partitioned at the oscillation source to formulate a synchronization perspective stability model incorporating dynamics of each converter and transmission network. Finally, the proposed method is validated by using a typical two-machine GFL-GFM system. With such method, the stability judgment failure caused by the feedback path aggregation is addressed, and the interaction mechanism between GFL and GFM synchronization dynamics as well as their parameter influences are revealed.
Engineering (General). Civil engineering (General), Chemical engineering
Sergii Sagin, Oleksandr Haichenia, Sergey Karianskyi
et al.
This paper aims to consider the issue of increasing the environmental friendliness of shipping by using alternative fuels in marine diesel engines. It has been determined that marine diesel engines are not only the main heat engines used on ships of sea and inland waterway transport, but are also sources of emissions of toxic components with exhaust gases. The main compounds whose emissions are controlled and regulated by international organizations are sulfur oxides (SO<sub>X</sub>) and nitrogen oxides (NO<sub>X</sub>), as well as carbon dioxide (CO<sub>2</sub>). Reducing NO<sub>X</sub> and CO<sub>2</sub> emissions while simultaneously increasing the environmental friendliness of shipping is possible by using fuel mixtures in marine diesel engines that include biodiesel fuel. During the research carried out on Wartsila 6L32 marine diesel engines (Shanghai Wartsila Qiyao Diesel Co. Ltd., Shanghai, China), RMG500 and DMA10 petroleum fuels were used, as well as their mixtures with biodiesel fuel FAME. It was found that when using mixtures containing 10–30% of FAME biodiesel, NO<sub>X</sub> emissions are reduced by 11.20–27.10%; under the same conditions, CO<sub>2</sub> emissions are reduced by 5.31–19.47%. The use of alternative fuels in marine diesel engines (one of which is biodiesel and fuel mixtures containing it) is one of the ways to increase the level of environmental sustainability of seagoing vessels and promote ecological shipping. This is of particular relevance when operating vessels in special ecological areas of the World Ocean. The relatively low energy intensity of the method of creating and using such fuel mixtures contributes to the spread of its use on many means of maritime transport.
Our Oceans cover more than 70% of the Earth’s surface, and thus various ocean engineering projects have been undertaken to utilize these vast resources effectively [...]
Cross-organizational collaboration in Model-Based Systems Engineering (MBSE) faces many challenges in achieving semantic alignment across independently developed system models. SysML v2 introduces enhanced structural modularity and formal semantics, offering a stronger foundation for interoperable modeling. Meanwhile, GPT-based Large Language Models (LLMs) provide new capabilities for assisting model understanding and integration. This paper proposes a structured, prompt-driven approach for LLM-assisted semantic alignment of SysML v2 models. The core contribution lies in the iterative development of an alignment approach and interaction prompts, incorporating model extraction, semantic matching, and verification. The approach leverages SysML v2 constructs such as alias, import, and metadata extensions to support traceable, soft alignment integration. It is demonstrated with a GPT-based LLM through an example of a measurement system. Benefits and limitations are discussed.
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.