Generative Artificial Intelligence (Gen AI) offers significant potential to support requirements engineering (RE) education; however, its integration poses challenges regarding accuracy and student engagement. While Gen AI cannot independently specify requirements without hallucinating or overstepping scope, it can serve as a powerful partner in a hybrid intelligence workflow. In this paper, we address the challenge of translating high-level motivational models into detailed user stories, a process that is traditionally labour-intensive for novices. We introduce a structured, human-in-the-loop workflow that uses Gen AI to refine and polish user stories while strictly preserving student agency. By grounding the output from Gen AI in a validated motivational model, the workflow minimises the risk of metacognitive offloading, requiring students to actively critique and validate the initially generated requirements. Our analysis of instructional artefacts demonstrates that Gen AI helps in three ways: suggesting structural improvements, offering alternative professional phrasing, and enhancing readability. However, we also identify risks of intent drift and scope expansion, reinforcing the need for rigorous human oversight. The findings advocate for a pedagogical approach where the Gen AI system acts as a reflective assistant rather than an autonomous generator.

Abstract The clean energy transition provides a unique opportunity to design a more just energy system. This paper introduces the Justice-Embedded Requirements Engineering (JERE) Process – an iterative process made to enable engineers to consider concepts of justice in their design of next generation technologies, with a focus on energy technologies. To assess the JERE Process, five teams (n=12) applied it to a project of their choosing and provided feedback through surveys and focus groups. JERE was found to elicit helpful conversations among teams and provide a structure for systematic engagement with justice considerations. Student researchers (n=7) generally found JERE to be more usable, appealing, and impactful compared to professional participants (n=5). Yet, overall, participants found the JERE tool prototype to be relatively difficult to use and found the length of the JERE Process and workshops challenging. Feedback from participants led to an updated version of the JERE Process that is simpler and modular. This study highlights difficulty engineers and researchers may face when attempting to practically embed justice principles in their technical design work and can assist others attempting to ensure technical solutions can support goals of a more just clean energy transition.

With the development of aerospace technology, hypersonic flight vehicles are evolving towards larger size, lighter weight, and higher performance. Their cross-domain maneuverability and extreme flight environment led to the rigid–flexible coupling effect and became the core bottleneck restricting performance improvement, seriously affecting flight stability and control accuracy. This paper systematically reviews the research status in the field of control for high-speed rigid–flexible coupling aircraft and conducts a review focusing on two core aspects: dynamic modeling and control strategies. In terms of modeling, the modeling framework based on the average shafting, the nondeformed aircraft fixed-coordinate system, and the transient coordinate system is summarized. In addition, the dedicated modeling methods for key issues, such as elastic mode coupling and liquid sloshing in the fuel tank, are also presented. The research progress and challenges of multi-physical field (thermal–structure–control, fluid–structure–control) coupling modeling are analyzed. In terms of control strategies, the development and application of linear control, nonlinear control (robust control, sliding mode variable structure control), and intelligent control (model predictive control, neural network control, prescribed performance control) are elaborated. Meanwhile, it is pointed out that the current research has limitations, such as insufficient characterization of multi-physical field coupling, neglect of the closed-loop coupling characteristics of elastic vibration, and lack of adaptability to special working conditions. Finally, the relevant research directions are prospected according to the priority of “near-term engineering requirements–long-term frontier exploration”, providing Refs. for the breakthrough of the rigid–flexible coupling control technology of the new-generation high-speed aircraft.

The article addresses the need to update environmental requirements for engines used on river passenger and sightseeing vessels in the context of implementing federal programs aimed at modernizing the transport system of the Russian Federation, one of the key objectives of which is the development of inland waterway transport. It is noted that ensuring environmental safety is a mandatory condition for the sustainable development of transport, including river transport. This task is addressed through the establishment of maximum permissible emission limits for harmful substances contained in the exhaust gases of internal combustion engines. A comparative analysis of domestic and foreign regulatory documents governing environmental requirements for internal combustion engines is carried out. Based on the results of this comparison, it is concluded that the current standards for exhaust gas toxicity and smoke opacity of river vessel engines require updating. The concept of revising regulatory values is presented, proposing the use of vessel grouping as a basis for emission standardization. Based on the results of field studies and statistical processing of ship inspection reports, updated standards for exhaust gas smoke opacity and toxicity of marine diesel engines are proposed. It is demonstrated that updating environmental requirements is particularly relevant for passenger and sightseeing vessels operating in large metropolitan areas.

The GOHIT platform is a web-based system designed to support the management of student competitions within higher-education institutions. The first version of the platform lacked structured requirements documentation, resulting in unclear user roles and inconsistent workflows. This study aimed to establish a lightweight yet disciplined requirements-engineering (RE) process for the redevelopment of GOHIT v2. Using a qualitative case-study approach, data were collected through semi-structured interviews and document analysis involving three key stakeholders: the founder, the developer, and the program advisor. Thematic analysis was applied to transform stakeholder inputs into structured, verifiable requirements based on IEEE 830 documentation principles. The process yielded seventeen functional requirements, each traceable to stakeholder sources and internally evaluated using IEEE 830 quality attributes. The resulting Software Requirements Specification demonstrated high levels of completeness, consistency, and traceability, confirming that a structured yet adaptable RE approach can be effective for small or academic development teams. This study contributes a replicable model for implementing lightweight requirements engineering in resource-limited settings and establishes a baseline for future validation and quality assurance using a test-based mechanism.

This paper presents the Circle-Mapping Transition and Weighted Red-Billed Blue Magpie Optimizer (CTWRBMO), designed to address significant challenges in 3D path planning for drones. Although the original Red-Billed Blue Magpie Optimizer (RBMO) algorithm features a simple structure, few parameters, and strong local search capability, making it well-suited for UAV path optimization, it suffers from insufficient population diversity, limited global search ability, and a tendency to fall into local optima in complex high-dimensional scenarios. To overcome these limitations, four enhancement strategies are introduced. Firstly, the Circle chaotic mapping strategy leverages the randomness and ergodicity of chaotic sequences to generate an initial population that is uniformly distributed. This enhancement improves population diversity from the beginning and provides a solid foundation for global optimization. Secondly, the ε parameter is dynamically adjusted to prioritize local refinement during the early stages of optimization. This adjustment enables rapid convergence toward potentially optimal areas. This parameter increases to enhance global search capabilities as the algorithm progresses, thereby broadening the optimization space and achieving a dynamic equilibrium. Additionally, a nonlinear dynamic weighting factor (wd) is incorporated into the position update formula. The algorithm’s ability to escape local optima is significantly improved by dynamically altering the weight ratio between historical optimal positions and the current position. Furthermore, an elite perturbation mechanism based on individual neighborhoods is implemented to generate candidate solutions using local information. This mechanism enhances the algorithm’s local exploration capabilities and improves the stability of preserving optimal solutions, supported by a greedy criterion for optimal retention. Experimental results show that the CTWRBMO algorithm significantly outperforms comparison algorithms in terms of optimization accuracy and convergence speed, demonstrating exceptional global optimization capabilities. Additional applications in UAV 3D path planning simulations evaluated paths based on length, threat avoidance efficiency, and smoothness. The results indicate that paths planned using CTWRBMO are shorter, safer, and smoother compared to those generated by the Harrier Hawks Optimization (HHO), African Vulture Optimization Algorithm (AVOA), Artificial Bee Colony (ABC) Algorithm, and the traditional Magpie Algorithm, effectively meeting practical engineering requirements for UAV 3D path planning.

Foamed concrete is a lightweight, environmentally friendly civil engineering material with excellent absorption capacity. It has been widely applied in engineering fields such as building thermal insulation and pore filling of underground buried pipelines. But the mechanical properties of existing foamed concrete cannot meet the engineering requirements for support, pressure relief and filling of weak surrounding rock. The mechanical properties of foamed concrete were improved with CNTs to prepare CNT foamed concrete (CNTFC) pressure-relieving filling materials. The effects of five factors (the fly ash (FA) incorporation rate, aggregate–cement ratio, water–binder ratio, CNT incorporation rate and foam volume fraction) on the density and 2:1 cylinder strength (the ratio of uniaxial compressive strength to apparent density), splitting tensile (the ratio of splitting tensile strength to apparent density) and specific strength of the CNTFC were analyzed. By combining stress–strain and scanning electron microscopy analyses, the mechanism of improvement of the mechanical strength of CNTFC due to CNTs was clarified. The results show that the foam volume fraction, water–binder ratio and aggregate–cement ratio are the top three factors affecting its strength, followed by the CNT incorporation rate and FA incorporation rate. Among the five influencing factors, only the incorporation of CNTs increases the 2:1 cylinder strength, splitting tensile strength and specific strength. When the doping rate is 0.05%, this ratio specifically refers to the mass of CNTs accounting for 0.05% of the mass of the total cementitious materials of cement and fly ash. At this doping dosage, compared with the condition without CNTs (0% doping dosage), the uniaxial compressive strength increased from 6.23 MPa to 7.18 MPa (with an increase rate of 15.3%). The splitting tensile strength increased from 0.958 MPa to 1.02 MPa (with an increase rate of 6.5%). The density only slightly increased from 0.98 g/cm3 to 1.0 g/cm3 (with an increase rate of 2.0%), achieving the balance of “high strength-low density”. CNTs and cement hydrates are interwoven into a network structure, and the mechanical properties of the CNTFC are effectively improved by the excellent nanoscopic tensile properties. Excessive doping of CNTs takes 0.05% as the threshold. Exceeding this doping dosage (such as 0.10% and 0.15%) leads to a decrease in its strength and ductility due to CNT agglomeration and deterioration of pore structure. And 0.05% is the ratio of the mass of CNTs to the total cementitious materials of cement and fly ash. At this doping dosage, CNTs are uniformly dispersed and can balance the strength and density of CNTFC. The optimum proportion of CNTs is 0.05%.

The thaw settling coefficient (TSC) of frozen soil serves as a crucial metric for assessing frozen soil thawing deformation. This work investigates the relationship of four parameters - freezing temperature (T), dry density ([Formula: see text]), water content (w), and overlying load (P) with the TSC of frozen soil. Utilizing a nonlinear prediction approach, multiple models are employed to predict the TSC values of frozen soil under varying influencing factors. Leveraging an experimental database comprising 841 data samples, the study conducts a series of sensitivity analyses to identify the most influential parameters in each nonlinear model and to determine the optimal performing model. Results from artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models exhibit superior accuracy in predicting the TSC of frozen soil. The ANFIS model undergoes validation twice using new databases, demonstrating its reliability in meeting engineering requirements. Employing the mutual information (MI) analysis method, the study quantifies the impact of different influencing factors on the TSC of frozen soil, offering valuable insights for predicting TSC of frozen soil.

Building the right software starts with understanding and answering key questions i.e. what is truly needed, checking the capability of the team on can they build, how they will build, will they be reimbursed correctly? These questions are summation of the requirement engineering or requirement analysis, an essential step of the software development methodology. As Natural Language Processing (NLP) grows more powerful, particularly with new Artificial Intelligence (AI) like Large Language Models (LLM), getting these requirements right is more necessary than ever. This paper proposes a new framework for requirement analysis formulated on structured methods like Delphi and Nominal Group Technique (NGT) gathering expert input and building a solid work. Our approach works step by step: it first checks a project's basic feasibility, then uses MEASUR’s Collateral Analysis to consider greater impact and risks. Next, it clarifies all aims using a KAOS goal model, and finally maps out all the parts and their connections in a Feature Diagram. A case study at the university of swat was carried out on NLP and LLMs based projects for requirement analysis. The approach was tested against traditional, radical, contemporary and agile techniques for requirement analysis. Our proposed framework performed 12% better than the traditional techniques, 17% better than agile and contemporary techniques and 14% better than Radical technique for Requirement analysis, proving its practical value for creating reliable and well planned NLP applications.

As an emerging desalination technology, capacitive deionization (CDI) has garnered significant attention due to its superior energy efficiency and performance metrics compared to conventional distillation and reverse osmosis (RO) techniques. Over the past decade, substantial advances have been achieved across multiple technical dimensions of CDI systems. This review focuses on two representative CDI architectures-hybrid capacitive deionization (HCDI) and flow-electrode capacitive deionization (FCDI)-highlighting their core innovation mechanisms: the ion storage behavior in HCDI and the continuous operation characteristics of flow electrodes in FCDI. We systematically examine recent research progress in critical areas including innovative electrode materials, optimized cell configurations, and expanded contaminant removal targets (e.g., heavy metals/organic pollutants), while delving into machine learning (ML)-driven strategies for operational parameter optimization and system performance prediction. Ultimately, by synthesizing these technological breakthroughs and aligning them with current engineering requirements, this review aims to facilitate the scalable implementation and industrial adoption of CDI technology within sustainable water treatment frameworks.

Filter mudcakes are produced by filtering and dewatering waste slurries from engineering construction and river dredging. The poor mechanical properties of filter mudcakes make them unsuitable for subgrade filling, but converting them into subgrade filling that meets road engineering requirements solves disposal problems. In this work, the performance of modified filter mudcakes (MFMs) was assessed through the optimisation of curing agents, assessment of engineering characteristics and centrifuge modelling. The solidification effect was found to be greatest when 8% ground granulated blast-furnace slag, 4% quicklime and 1% gypsum were added to the filter mudcake. For this mix, the compressive strength was 2730 kPa after 7 days and the water stability coefficient was 85%. The California bearing ratio of these filter mudcakes was 33.7% at 7 days, and the compression coefficient (<0.1 MPa−1) and the permeability coefficient (5.39 × 10−8 cm/s) both met the specification requirements at a dosage of 4%. As the degree of compaction was increased from 88% to 97%, the final settlement of the new MFM subgrade at the centre decreased from 50.9 mm to 35.4 mm, and that of the shoulder decreased from 42.3 mm to 29.6 mm, meeting specification requirements (<100 mm). The results support the excellent long-term stability and application potential of MFMs as subgrade materials.

Cracks serve as a critical indicator of tunnel structural degradation. Manual inspections are difficult to meet engineering requirements due to their time-consuming and labor-intensive nature, high subjectivity, and significant error rates, while traditional image processing methods exhibit poor performance under complex backgrounds and irregular crack morphologies. To address these limitations, this study developed a high-quality dataset of tunnel crack images and proposed an improved lightweight semantic segmentation network, LiteSqueezeSeg, to enable precise crack identification and quantification. The model was systematically trained and optimized using a dataset comprising 10,000 high-resolution images. Experimental results demonstrate that the proposed model achieves an overall accuracy of 95.15% in crack detection. Validation on real-world tunnel surface images indicates that the method effectively suppresses background noise interference and enables high-precision quantification of crack length, average width, and maximum width, with all relative errors maintained within 5%. Furthermore, an integrated intelligent detection system was developed based on the MATLAB (R2023b) platform, facilitating automated crack feature extraction and standardized defect grading. This system supports routine tunnel maintenance and safety assessment, substantially enhancing both inspection efficiency and evaluation accuracy. Through synergistic innovations in lightweight network architecture, accurate quantitative analysis, and standardized assessment protocols, this research establishes a comprehensive technical framework for tunnel crack detection and structural health evaluation, offering an efficient and reliable intelligent solution for tunnel condition monitoring.

From the perspective of civil air defense engineering, the fundamental goal of construction is to realize social benefits and combat readiness benefits. In this regard, continuously improving the quality of civil air defense engineering and presenting excellent peacetime and wartime functions have become the top priority. This study conducts in-depth investigation and analysis on multiple civil air defense engineering projects in Guizhou Province, and finds that there are still many problems, which will directly affect the overall quality of civil air defense engineering and deviate to a certain extent from the combat readiness requirements of civil air defense engineering. Therefore, it is necessary to sort out and summarize the relevant problems, and formulate scientific and feasible solutions through discussion and research.

The design of slab foundations relies on the accurate assessment of the interaction between the structure and the soil base, particularly on the reliable determination of contact stresses. The choice of calculation method directly affects the results of bearing capacity evaluation and the prediction of soil deformations. Analytical approaches, such as the corner points method, are traditionally widely used in engineering practice due to their simplicity and relatively low labor intensity. However, these methods are based on simplified assumptions regarding the stress–strain state of the soil, which can lead to errors under specific conditions, such as non-uniform loading, complex geometry, or heterogeneous soil composition. Modern numerical methods, particularly finite element modeling, allow for a more detailed consideration of soil behavior, including elastic–plastic properties and realistic boundary conditions. Nevertheless, they require significant computational resources, expertise in specialized software, and comprehensive input data on soil properties. Therefore, a comparative study of the results obtained using the analytical corner points method and numerical modeling is relevant both for verifying the accuracy and applicability limits of traditional approaches and for refining influence coefficients to adapt calculation methods to current engineering requirements. Understanding the quantitative and qualitative differences between these methods will enable engineers to make informed decisions when selecting a calculation approach depending on the design conditions, while also optimizing the balance between accuracy, available resources, and calculation time. The article presents a comparison of contact stresses in a soil base under a rectangular slab foundation obtained by the analytical corner points method and finite element numerical modeling for elastic–linear and elastic–plastic soil models (with the Mohr–Coulomb strength criterion). The aim of the study is to refine the influence coefficients used in the corner points method, to identify quantitative and qualitative differences between the approaches, and to outline the boundaries of their correct application. The comparison revealed significant discrepancies not only in the values of contact stresses but also in the shape of their distribution surface. Preliminary discrepancy coefficients for the studied case have been determined.

The increase of mobile apps has led to an exponential growth of user-generated reviews, which are often noisy, informal, and linguistically diverse, thereby posing significant challenges for automated analysis in requirements engineering. This study evaluates whether back-translation (BT) can normalize informal reviews while preserving meaning, and which model (Google Translate vs Facebook M2M100_418M) offers better semantic preservation, grammatical quality, and lexical alignment. We collected 323 Google Play reviews (667 sentences) from three Malaysian government apps. Texts were cleaned, expanded for colloquial forms, and then BT was applied using Malay as an intermediate language. Evaluation used four metrics which are semantic similarity (Sentence-BERT), grammar error count (LanguageTool), BLEU (NLTK), and perplexity (GPT-2). Models differences were tested with paired t-tests and Wilcoxon signed- rank tests, while paired scatterplots showed distributional patterns. Google was significantly better on semantic similarity (t(322)=5.38, p<.001), grammar errors (t(322)=3.66, p<.001), and BLEU (t(322)=2.99, p=.003); effect sizes were small to moderate. Perplexity differences were not significant, indicating comparable sentence-level fluency. Visualizations confirmed Google’s steadier performance with fewer extreme outliers. BT is a practical normalization step for noisy reviews. For the English–Malay pipeline studied here, Google provides more reliable semantic preservation and grammatical quality, while both systems are similar in fluency. However, the generalizability of these results are constrained by the relatively modest sample size (323 reviews, 667 sentences), and future work should validate results on large datasets and explore hybrid strategies combining strengths of both models.

The diaphragm wall, as a key supporting structure in bridge anchorage projects, faces complex and variable stress conditions during construction. Therefore, ensuring structural safety and performance monitoring is crucial. Traditional rebar monitoring methods, due to poor corrosion resistance, insufficient real-time capabilities, and maintenance difficulties, cannot meet the high precision and reliability requirements of modern underground engineering. This study applies self-sensing technology to fiber-reinforced polymer (FRP) materials by embedding optical fiber sensors within the FRP, enhancing real-time monitoring of stress and strain during the construction of diaphragm walls. This technology has been successfully implemented in the Shiziyang Bridge project, enabling real-time monitoring of stress and strain in the anchor diaphragm wall. The study adopts a quasi-distributed optical fiber monitoring scheme, combined with wireless transmission and a cloud platform for remote data acquisition and analysis. The results indicate that the self-sensing FRP bar shows excellent stress and strain monitoring capabilities at various stages of diaphragm wall construction. In stages 1–3, the stress curve transitions from tensile stress to alternating tensile and compressive stress. The shallow and mid-layers exhibit tensile stress, while the deep layers experience compressive stress. The maximum tensile stress recorded is 35.8 MPa, and the maximum compressive stress is -20.3 MPa, mainly due to pressure imbalance caused by soil excavation and the decreasing groundwater level. In stage 4, the upper stress gradually decreases, while the lower stress transitions from tensile to compressive, with the maximum tensile stress at 13.9 MPa and the maximum compressive stress at − 37.7 MPa. These changes are attributed to the completion of the liner and bottom slab construction, backfilling of the soil, and the increased self-weight of the upper structure. In stage 5, as construction progresses, the stress curve forms an M-shape, with compressive stress gradually decreasing. The maximum tensile stress is 2.3 MPa, and the maximum compressive stress is − 11.8 MPa, mainly influenced by the increasing tensile force applied by the stay cables. As of November 14, 2024, the monitoring data show that the tensile strain in the shallow layers remains unchanged, while the compressive strain in the middle and deep layers is gradually decreasing.

The stable operation of a rolling mill is crucial for the extremely thin strip rolling process. Moreover, the performance of the rolling mill directly dictates the quality of the extremely thin strip products. In view of the lack of research on the digital twin model and condition monitoring of twenty-high rolling mills, this paper takes the Sendzimir 280 mm twenty-high reversible rolling mill, an extremely thin strip rolling equipment, as the research object, and conducts digital twin modeling and visualization design for it. First and foremost, finite element analysis and vibration analysis were conducted on the rolling mill, based on which the finite element model and dynamics model of the twenty-high rolling mill were established. Secondly, through a comparison between the vibration data of the rolling mill obtained from simulation and those of the physical rolling mill, the accuracy of the simulation model was validated. Finally, a digital twin model of the rolling mill was constructed based on the finite element model and the dynamics model, and the digital twin model of the rolling mill was built using Unity (version 2022.3.57, Unity Technologies, San Francisco, CA, USA) software to complete the visualization design of the digital twin model. The results show that the digital twin platform of the rolling mill established in this paper achieves a high degree of similarity between the virtual rolling mill and the physical one, which proves the effectiveness of the platform and can meet the actual engineering requirements.

Abstract Context: Requirements Engineering (RE)-related activities are critical in developing quality software and one of the most human-dependent processes in software engineering (SE). Hence, identifying the impact of diverse human-related aspects on RE is crucial in the SE context. Objective: Our study explores the impact of one of the most influential human aspects, motivation on RE, aiming to deepen understanding and provide practical guidance. Method: By conducting semi-structured interviews with 21 RE-involved practitioners, we developed a theory using socio-technical grounded theory (STGT) that explains the contextual, causal, and intervening conditions influencing motivation in RE-related activities. Result: We identified strategies to enhance motivating situations or mitigate demotivating ones, and the consequences resulting from applying these strategies. Conclusion: Our findings offer actionable insights for software practitioners to manage the influence of motivation on RE and help researchers further investigate its role across various SE contexts in the future.

Abstract Optimization is a fundamental requirement in modern engineering and computational systems, where efficient utilization of resources and reduction of computational cost are critical. Graph theory offers a robust mathematical framework for modeling and solving optimization problems by representing system elements as vertices and their interactions as weighted or capacitated edges. This paper investigates the application of graph-theoretic optimization techniques and their implementation using MATLAB. Classical optimization problems, including shortest path, minimum spanning tree, and maximum flow, are formulated using graph models and solved through well-established algorithms. MATLAB’s graph and network analysis tools are employed to implement these algorithms, enabling efficient computation, visualization, and validation of optimal solutions. The proposed methodology demonstrates how graph-based modeling simplifies complex optimization problems and reduces computational complexity while maintaining solution accuracy. Through illustrative examples and performance evaluation, the effectiveness of graph theory–based optimization is demonstrated in practical domains such as network design, transportation systems, and resource allocation. The results confirm that integrating graph theory with MATLAB provides a scalable and efficient framework for addressing large-scale optimization problems. This study highlights the significance of graph-theoretic approaches as reliable optimization tools for contemporary engineering and scientific applications. Keywords: Graph Theory, Optimization Techniques, Shortest Path, Minimum Spanning Tree, Maximum Flow, MATLAB

To solve the problem that traditional line loss calculation methods have errors exceeding 8–12% under complex weather conditions (e.g., typhoons) due to insufficient characterization of meteorological-photovoltaic (PV) coupling effects, this paper proposes a collaborative calculation method integrating dynamic meteorological clustering (based on the entropy weight-sliding window) and PV output reconstruction (via improved limited dynamic time warping, LDTW). First, a multidimensional meteorological weight matrix is constructed to quantify spatiotemporal heterogeneity; then, an improved spectral clustering algorithm is used for weather partitioning; finally, reconstructed PV output curves are incorporated into a voltage-corrected forward-backward sweep method for line loss calculation. Simulation results based on 302-day measured data and the IEEE 33-node system show that the proposed method reduces line loss calculation error to less than 0.15%, which is 6–8 times more accurate than traditional methods, meeting engineering requirements.