The development of bimetal based catalysts via interfacial engineered strategy has been intensively explored due to its great potential for enhancing the electrochemical performance. The significant progress achieved by the interfacial engineering is mainly derived from its great ability on tuning the intermediate adsorption, controlling the electron and mass transportation, preventing catalysts from serious aggregation, as well as providing advanced promoter for the rational design of highly efficient catalysts. Here, the recent works on the interfacial engineered strategy for developing highly efficient bimetal based electrocatalysts are outlined. The advantages of interfacial engineered strategy on manipulating the activity, selectivity, and stability of catalysts are first discussed. The recent synthetic approaches for controlling the interface structures and the related hydrogen evolution reaction, oxygen evolution reaction, oxygen reduction reaction, and electroreduction of carbon dioxide are elaborated based on three major categories, involving metal/metal, metal/metal compound, and metal/support interfaces. Challenges and perspectives of this field are represented in the final section.
Tanja E. J. Vos, Tijs van der Storm, Alexander Serebrenik
et al.
Software engineering is the invisible infrastructure of the digital age. Every breakthrough in artificial intelligence, quantum computing, photonics, and cybersecurity relies on advances in software engineering, yet the field is too often treated as a supportive digital component rather than as a strategic, enabling discipline. In policy frameworks, including major European programmes, software appears primarily as a building block within other technologies, while the scientific discipline of software engineering remains largely absent. This position paper argues that the long-term sustainability, dependability, and sovereignty of digital technologies depend on investment in software engineering research. It is a call to reclaim the identity of software engineering.
PURPOSE OR GOAL: This study investigates how GenAI can be integrated with a criterion-referenced grading framework to improve the efficiency and quality of grading for mathematical assessments in engineering. It specifically explores the challenges demonstrators face with manual, model solution-based grading and how a GenAI-supported system can be designed to reliably identify student errors, provide high-quality feedback, and support human graders. The research also examines human graders' perceptions of the effectiveness of this GenAI-assisted approach. ACTUAL OR ANTICIPATED OUTCOMES: The study found that GenAI achieved an overall grading accuracy of 92.5%, comparable to two experienced human graders. The two researchers, who also served as subject demonstrators, perceived the GenAI as a helpful second reviewer that improved accuracy by catching small errors and provided more complete feedback than they could manually. A central outcome was the significant enhancement of formative feedback. However, they noted the GenAI tool is not yet reliable enough for autonomous use, especially with unconventional solutions. CONCLUSIONS/RECOMMENDATIONS/SUMMARY: This study demonstrates that GenAI, when paired with a structured, criterion-referenced framework using binary questions, can grade engineering mathematical assessments with an accuracy comparable to human experts. Its primary contribution is a novel methodological approach that embeds the generation of high-quality, scalable formative feedback directly into the assessment workflow. Future work should investigate student perceptions of GenAI grading and feedback.
The quantum threat to cybersecurity has accelerated the standardization of Post-Quantum Cryptography (PQC). Migrating legacy software to these quantum-safe algorithms is not a simple library swap, but a new software engineering challenge: existing vulnerability detection, refactoring, and testing tools are not designed for PQC's probabilistic behavior, side-channel sensitivity, and complex performance trade-offs. To address these challenges, this paper outlines a vision for a new class of tools and introduces the Automated Quantum-safe Adaptation (AQuA) framework, with a three-pillar agenda for PQC-aware detection, semantic refactoring, and hybrid verification, thereby motivating Quantum-Safe Software Engineering (QSSE) as a distinct research direction.
Abstract Natural biomaterials are extensively used in tissue engineering due to their microstructure interconnectivity and inherent bioactivity which mimics of natural extracellular matrix (ECM), supporting cell infiltration, adhesion, differentiation, transportation of oxygen and nutrient, finally restoring the structure and function of defective tissues or organs. Microstructure, mechanical properties, biostability and cellular activity of natural biomaterials are controlled via blending of natural or natural with synthetic biopolymers and physical/chemical crosslinking treatments to allow the required mechanical strength, degradation rate and ECM mimic microenvironment for supporting of cellular activity. In addition, natural biomaterials also performed a key role in delivery of cells, bioactive molecules, growth factors and drugs. In this review, we will explore the fabrication, challenges and applications of natural biomaterials for various tissues engineering issues, including polymer selection, fabrication techniques, microstructure manipulation, physical/chemical crosslinking, mechanical properties, biostability as well as their role in delivery of cells, bioactive molecules, growth factors and drugs.
The low pozzolanic activity of the silt in the Yellow River has always been the key factor that hinders its resource utilization as artificial stones. A high-speed ball-milling method was first attempted to pretreat the silt for enhancing its pozzolanic activity in this study. The artificial stones incorporating the ball-milled silt were prepared by the alkali-activation method. The changes in the basic physical and chemical properties and microstructures of the pre-treated silt and fabricated artificial stones were characterized. Also, the compressive strength, splitting tensile strength and water resistance of fabricated specimens were measured. The results show that the ball-milling treatment destroys the original crystal structure, and significantly enhances the potential pozzolanic activity of the silt. In addition, it improves the compressive strength, splitting tensile strength and water resistance of the artificial stones. The compressive strength of artificial stones incorporated with the ball-milled silt has a maximum value of 17.1 MPa, showing an increase by one time. It can be ascribed to the increase of C-S-H/C-A-S-H gel amount in specimens. Furthermore, compared to traditional mined bluestone, the carbon emission of the alkali-activated artificial stone incorporating ball-milled Yellow River silt is much lower. The production of 1 t of artificial stone only emits 1155.25 kg of CO2.
Materials of engineering and construction. Mechanics of materials
[Objective] Evacuation bottlenecks for subway stations in flood scenarios are investigated to enhance subway station personnel evacuation efficiency under water inundation and backflow conditions, and provide insights for developing subway evacuation emergency plans. [Method] Pedestrian walking tests are conducted using drone aerial footage. OpenCV (open source computer vision) library image processing algorithms are employed to analyze pedestrians walking paths and calculate their walking speeds in carious water depth scenarios. Based on the social force model and in line with subway safety evacuation standards, an emergency evacuation simulation model for subway stations in flood scenarios is established and then simulated using Anylogic. [Result & Conclusion] The test results show that the deeper the water depth, the slower the pedestrian passage speed, and the greater the amplitude of the left-right swing of the pedestrians to maintain balance. If support structures are added in critical areas, pedestrian walking efficiency can be effectively improved. Taking a sunken plaza subway station in a coastal city as an example, different degrees of inundation and backflow are analyzed through simulation. Results show that the water depth has a significant impact on the walking speed and evacuation efficiency of pedestrians. When water depth is over 5 cm, the evacuation time may exceed the requirements of evacuation specifications. When water depth reaches 20 cm, the proportion of people safely evacuated within 6 minutes is 26.38%. Failure to organize timely and effective evacuation during the flood event will seriously threaten the evacuation safety of the subway station.
The paper entitled "Qualitative Methods in Empirical Studies of Software Engineering" by Carolyn Seaman was published in TSE in 1999. It has been chosen as one of the most influential papers from the third decade of TSE's 50 years history. In this retrospective, the authors discuss the evolution of the use of qualitative methods in software engineering research, the impact it's had on research and practice, and reflections on what is coming and deserves attention.
Foundation models (FMs), particularly large language models (LLMs), have shown significant promise in various software engineering (SE) tasks, including code generation, debugging, and requirement refinement. Despite these advances, existing evaluation frameworks are insufficient for assessing model performance in iterative, context-rich workflows characteristic of SE activities. To address this limitation, we introduce \emph{SWE-Arena}, an interactive platform designed to evaluate FMs in SE tasks. SWE-Arena provides a transparent, open-source leaderboard, supports multi-round conversational workflows, and enables end-to-end model comparisons. The platform introduces novel metrics, including \emph{model consistency score} that measures the consistency of model outputs through self-play matches, and \emph{conversation efficiency index} that evaluates model performance while accounting for the number of interaction rounds required to reach conclusions. Moreover, SWE-Arena incorporates a new feature called \emph{RepoChat}, which automatically injects repository-related context (e.g., issues, commits, pull requests) into the conversation, further aligning evaluations with real-world development processes. This paper outlines the design and capabilities of SWE-Arena, emphasizing its potential to advance the evaluation and practical application of FMs in software engineering.
Marc Bruni, Fabio Gabrielli, Mohammad Ghafari
et al.
Prompt engineering reduces reasoning mistakes in Large Language Models (LLMs). However, its effectiveness in mitigating vulnerabilities in LLM-generated code remains underexplored. To address this gap, we implemented a benchmark to automatically assess the impact of various prompt engineering strategies on code security. Our benchmark leverages two peer-reviewed prompt datasets and employs static scanners to evaluate code security at scale. We tested multiple prompt engineering techniques on GPT-3.5-turbo, GPT-4o, and GPT-4o-mini. Our results show that for GPT-4o and GPT-4o-mini, a security-focused prompt prefix can reduce the occurrence of security vulnerabilities by up to 56%. Additionally, all tested models demonstrated the ability to detect and repair between 41.9% and 68.7% of vulnerabilities in previously generated code when using iterative prompting techniques. Finally, we introduce a "prompt agent" that demonstrates how the most effective techniques can be applied in real-world development workflows.
Shavindra Wickramathilaka, John Grundy, Kashumi Madampe
et al.
The use of diverse mobile applications among senior users is becoming increasingly widespread. However, many of these apps contain accessibility problems that result in negative user experiences for seniors. A key reason is that software practitioners often lack the time or resources to address the broad spectrum of age-related accessibility and personalisation needs. As current developer tools and practices encourage one-size-fits-all interfaces with limited potential to address the diversity of senior needs, there is a growing demand for approaches that support the systematic creation of adaptive, accessible app experiences. To this end, we present AdaptForge, a novel model-driven engineering (MDE) approach that enables advanced design-time adaptations of mobile application interfaces and behaviours tailored to the accessibility needs of senior users. AdaptForge uses two domain-specific languages (DSLs) to address age-related accessibility needs. The first model defines users' context-of-use parameters, while the second defines conditional accessibility scenarios and corresponding UI adaptation rules. These rules are interpreted by an MDE workflow to transform an app's original source code into personalised instances. We also report evaluations with professional software developers and senior end-users, demonstrating the feasibility and practical utility of AdaptForge.
Qiaolin Qin, Ronnie de Souza Santos, Rodrigo Spinola
Context. The rise of generative AI (GenAI) tools like ChatGPT and GitHub Copilot has transformed how software is learned and written. In software engineering (SE) education, these tools offer new opportunities for support, but also raise concerns about over-reliance, ethical use, and impacts on learning. Objective. This study investigates how undergraduate SE students use GenAI tools, focusing on the benefits, challenges, ethical concerns, and instructional expectations that shape their experiences. Method. We conducted a survey with 130 undergraduate students from two universities. The survey combined structured Likert-scale items and open-ended questions to investigate five dimensions: usage context, perceived benefits, challenges, ethical and instructional perceptions. Results. Students most often use GenAI for incremental learning and advanced implementation, reporting benefits such as brainstorming support and confidence-building. At the same time, they face challenges including unclear rationales and difficulty adapting outputs. Students highlight ethical concerns around fairness and misconduct, and call for clearer instructional guidance. Conclusion. GenAI is reshaping SE education in nuanced ways. Our findings underscore the need for scaffolding, ethical policies, and adaptive instructional strategies to ensure that GenAI supports equitable and effective learning.