This paper presents a novel surrogate modeling approach for estimating the dynamic radar cross-section (RCS) of chaff clouds under diverse launch and environmental conditions. A high-fidelity computational fluid dynamic–discrete element method (CFD-DEM) framework is first used to simulate the multiphysics behavior of chaff clouds generated by both naval and aircraft dispensers. These simulations generate detailed aerodynamic datasets, which are used to train a Gaussian process regression (GPR)–based surrogate model. The surrogate model enables efficient prediction of the spatiotemporal distribution of chaff clouds, incorporating variables such as wind speed, wind direction, and launch parameters. To estimate dynamic RCS, the spatiotemporal distributions are combined with approximation techniques, specifically the generalized equivalent conductor (GEC) and vector radiative transfer (VRT) methods. A real-time chaff cloud simulator with a graphical user interface is also developed, integrating aerodynamic modeling, RCS calculations, and signal fluctuation modeling. Simulation results demonstrate that the proposed surrogate model achieves high prediction accuracy, with normalized mean absolute errors (NMAE) of 0.0085 for naval chaff and 0.0176 for aircraft chaff. The dynamic RCS obtained via the surrogate model closely matches the CFD-DEM results while substantially reducing computational cost, thus offering practical utility for real-time system applications.
Multiaspect polarimetric synthetic aperture radar (SAR) captures the polarimetric properties of targets from various observational aspects. The comprehensive multiaspect scattering characteristics are valuable for man-made structure detection and classification. Typically, the anisotropic scattering of targets could be characterized by the differences in the statistical properties of polarimetric data across aspects. However, both the statistical similarities in man-made structures and variabilities in natural targets at different aspects can negatively impact the ability to distinguish between them. Consequently, relying solely on anisotropic analysis may not yield favorable man-made structure detection results. Since man-made structures usually include special shapes, such as dihedral angle, there are significant variations in scattering power across different aspects. Therefore, this article proposes an improved man-made structure detection method that integrates scattering power characteristics and anisotropic features. First, to highlight differences between aspects, this article introduces a similarity matrix to perform azimuth sequence filtering. Subsequently, anisotropic features are extracted through differences in statistical distribution, and scattering power characteristics at individual aspects, along with their variations, are extracted using the fuzzy C-means clustering combined with spatial neighborhood. Two different features are fused to distinguish man-made structures from natural targets. Finally, the most significant azimuth aspect is determined by comparing the scattering contributions of individual subapertures. Experimental verification with airborne circular polarimetric SAR data confirms that the multifeature fusion method, following azimuth sequence filtering, effectively improves the detection of man-made structures and their most anisotropic subapertures.
The living quarters (LQ) on jack-up rigs play a critical role in ensuring crew safety and operational functionality under extreme offshore conditions. This study presents a comprehensive structural engineering procedure for the design and analysis of LQ structures, addressing the absence of specific industry standards. The methodology integrates global and local load effects from critical equipment, such as helidecks and lifeboat stations, under harsh environmental conditions during wet towing. A multi-level analysis approach, including finite element analysis (FEA), nonlinear evaluations, and fatigue assessments, was employed to verify structural resilience. The study successfully validates the LQ structures against ultimate limit state (ULS), serviceability limit state (SLS), and accidental limit state (ALS) criteria. The maximum plastic strain observed under green water pressure was 3.8 %, well below the allowable threshold of 15 %, demonstrating adequate safety margins. Fatigue analysis confirmed resistance to vortex-induced vibrations (VIV), ensuring the durability of tubular members. Optimization efforts reduced LQ structural weight by 20 %, enhancing efficiency without compromising safety. The proposed procedure bridges the gap in industry standards, providing a robust framework for designing safer and more reliable LQ structures. This study advances offshore engineering practices by addressing complex loading scenarios and operational challenges, thereby supporting the development of resilient jack-up rigs capable of enduring extreme marine conditions.
This study investigated the effects of galactooligosaccharide (GOS) purity (80 % and 95 %) and heating time (60 and 150 min) on characterization and bioactivities of Maillard reaction products (MRPs) formed by conjugating tilapia scale collagen peptides with GOS at 90 °C. Spectral analyses, degree of grafting, and furosine content showed that lower-purity GOS exhibited higher glycation reactivity. Amino acid composition and advanced glycation end products analyses showed that arginine was the primary amino acid involved in glycation, with abundant formation of methylglyoxal-derived hydroimidazolone 1 (MG-H1). Digested MRPs maintained strong antioxidant activity, particularly in the lower-purity GOS group. Meanwhile, MRPs heated for 150 min enhanced L. casei, L. pentosus, and B. bifidum growth, while those heated for 60 min favored B. longum. MG-H1 level was positively correlated with antioxidant activity and B. bifidum growth (p < 0.01). This study highlights the broad application potential of glycated bioactive peptides with lower-purity GOS.
Nutrition. Foods and food supply, Food processing and manufacture
The conventional dynamic fracture simulation by using the explicit algorithm often involves a large number of iteration computation due to the extremely small time interval. Thus, the most time-consuming process is the integration of constitutive relation. To improve the efficiency of the dynamic fracture simulation, a physics-informed neural network integration (PINNI) model is developed to calculate the integration of constitutive relation. PINNI employs a shallow multilayer perceptron with integrable activations to approximate constitutive integrand. To train PINNI, a large number of strains in a reasonable range are generated at first, and then the corresponding stresses are calculated by the mechanical constitutive relation. With the generated strains as input data and the calculated stresses as output data, the PINNI can be trained to reach a very high precision, whose relative error is about <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>7.8</mn><mo>×</mo><msup><mrow><mn>10</mn></mrow><mrow><mo>−</mo><mn>5</mn></mrow></msup></mrow></semantics></math></inline-formula>%. Next, the mechanical integration of constitutive relation is replaced by the well-trained PINNI to perform the dynamic fracture simulation. It is found that the simulation results by the mechanical and PINNI approach are almost the same. This suggests that it is feasible to use PINNI to replace the rigorous mechanical integration of constitutive relation. The computational efficiency is significantly enhanced, especially for the complicated constitutive relation. It provides a new AI-combined approach to dynamic fracture simulation.
Tidal energy has emerged as a promising renewable energy source, with abundant marine resources available in many parts of the world. To exploit this resource efficiently, reliable and computationally efficient methods are required to analyze energy yields from tidal arrays in real sites worldwide. This paper investigates the impact of irregular-bathymetry seabed elements near a tidal turbine location on the turbine’s performance and wake. A high-resolution three-dimensional bathymetry model was created, and full-scale unsteady simulations were performed using the ANSYS-Fluent computational fluid dynamics tool and the Shear Stress Turbulence (SST) model for two cases: one with the site bathymetry and one with a flat seabed. Compared to the flatbed case, the results show a 1.84% increase in the average turbine power output for the site-bathymetry case. A 4.1% increase in average wake recovery rate was observed near the hill-like seabed features from 3D to 7D downstream from the turbine, followed by 11% reduction in wake recovery rate over the bathymetry slope from 9D downstream from the turbine. The findings of this study highlight the implications of bathymetry-generated effects in optimal site selection and tidal energy farm design.
A well-rounded software engineer is often defined by technical prowess and the ability to deliver on complex projects. However, the narrative around the ideal Software Engineering (SE) candidate is evolving, suggesting that there is more to the story. This article explores the non-technical aspects emphasized in SE job postings, revealing the sociotechnical and organizational expectations of employers. Our Thematic Analysis of 100 job postings shows that employers seek candidates who align with their sense of purpose, fit within company culture, pursue personal and career growth, and excel in interpersonal interactions. This study contributes to ongoing discussions in the SE community about the evolving role and workplace context of software engineers beyond technical skills. By highlighting these expectations, we provide relevant insights for researchers, educators, practitioners, and recruiters. Additionally, our analysis offers a valuable snapshot of SE job postings in 2023, providing a scientific record of prevailing trends and expectations.
Timo Kehrer, Robert Haines, Guido Juckeland
et al.
Anecdotal evidence suggests that Research Software Engineers (RSEs) and Software Engineering Researchers (SERs) often use different terminologies for similar concepts, creating communication challenges. To better understand these divergences, we have started investigating how SE fundamentals from the SER community are interpreted within the RSE community, identifying aligned concepts, knowledge gaps, and areas for potential adaptation. Our preliminary findings reveal opportunities for mutual learning and collaboration, and our systematic methodology for terminology mapping provides a foundation for a crowd-sourced extension and validation in the future.
Lekshmi Murali Rani, Richard Berntsson Svensson, Robert Feldt
The integration of AI for Requirements Engineering (RE) presents significant benefits but also poses real challenges. Although RE is fundamental to software engineering, limited research has examined AI adoption in RE. We surveyed 55 software practitioners to map AI usage across four RE phases: Elicitation, Analysis, Specification, and Validation, and four approaches for decision making: human-only decisions, AI validation, Human AI Collaboration (HAIC), and full AI automation. Participants also shared their perceptions, challenges, and opportunities when applying AI for RE tasks. Our data show that 58.2% of respondents already use AI in RE, and 69.1% view its impact as positive or very positive. HAIC dominates practice, accounting for 54.4% of all RE techniques, while full AI automation remains minimal at 5.4%. Passive AI validation (4.4 to 6.2%) lags even further behind, indicating that practitioners value AI's active support over passive oversight. These findings suggest that AI is most effective when positioned as a collaborative partner rather than a replacement for human expertise. It also highlights the need for RE-specific HAIC frameworks along with robust and responsible AI governance as AI adoption in RE grows.
Baicalin, a flavonoid extracted from traditional Chinese medicine, Scutellaria baicalensis has significant anti-inflammatory effects. Microsponges are drug delivery systems that improve drug stability and slow the release rate. The combination of baicalin and the microsponges produced a new and stable system for its delivery, resulting in a novel formulation of baicalin. Baicalin microsponges (BM) were prepared using the quasi-emulsion solvent diffusion method. Effects of the mass ratio of the polymer (ethylcellulose) to baicalin, the concentration of the emulsifier polyvinyl alcohol (PVA), the stirring speed on the encapsulation efficiency (EE), and yield of the microsponges were investigated by combining the one-factor test and Box-Behnken design (BBD). The preparation process was standardised using 2.61:1 mass ratio of ethyl cellulose to baicalin, 2.17% concentration of PVA, with stirring at 794 rpm. Optimised BM formulations were evaluated for the parameters of EE (54.06 ± 3.02)% and yield of (70.37 ± 2.41)%, transmission electron microscopy (TEM), and in vitro cell evaluation. Results of the in vitro anti-inflammatory assay showed that baicalin microsponges-pretreated-lipopolysaccharide (LPS)-induced RAW264.7, mouse macrophages showed reduced inflammatory response, similar to that seen in baicalin-treated macrophages.
In this manuscript, we will apply the regularized meshless method, coupled with an error estimation technique, to tackle the challenge of modeling oblique incident waves interacting with multiple cylinders. Given the impracticality of obtaining an exact solution in many real engineering problems, we introduce an error estimation technique designed to achieve reliable solutions. This technique excels in providing dependable solutions that closely approximate analytical solutions. An additional advantage is its capacity to identify the optimal number of points for both source and collocating points, thereby enhancing computational efficiency. The validity of the proposed method will be demonstrated through three numerical cases, presenting results that exhibit substantial agreement.
In this paper we discuss the growing need for system behaviour to be validated and verified (V&V'ed) early in model-based systems engineering. Several aspects push companies towards integration of techniques, methods, and processes that promote specific and general V&V activities earlier to support more effective decision-making. As a result, there are incentives to introduce new technologies to remain competitive with the recently drastic changes in system complexity and heterogeneity. Performing V&V early on in development is a means of reducing risk for later error detection while moving key activities earlier in a process. We present a summary of the literature on early V&V and position existing challenges regarding potential solutions and future investigations. In particular, we reason that the software engineering community can act as a source for inspiration as many emerging technologies in the software domain are showing promise in the wider systems domain, and there already exist well formed methods for early V&V of software behaviour in the software modelling community. We conclude the paper with a road-map for future research and development for both researchers and practitioners to further develop the concepts discussed in the paper.
Oleksandr Kosenkov, Michael Unterkalmsteiner, Jannik Fischbach
et al.
Context: Regulatory acts are a challenging source when eliciting, interpreting, and analyzing requirements. Requirements engineers often need to involve legal experts who, however, may often not be available. This raises the need for approaches to regulatory Requirements Engineering (RE) covering and integrating both legal and engineering perspectives. Problem: Regulatory RE approaches need to capture and reflect both the elementary concepts and relationships from a legal perspective and their seamless transition to concepts used to specify software requirements. No existing approach considers explicating and managing legal domain knowledge and engineering-legal coordination. Method: We conducted focus group sessions with legal researchers to identify the core challenges to establishing a regulatory RE approach. Based on our findings, we developed a candidate solution and conducted a first conceptual validation to assess its feasibility. Results: We introduce the first version of our Artifact Model for Regulatory Requirements Engineering (AM4RRE) and its conceptual foundation. It provides a blueprint for applying legal (modelling) concepts and well-established RE concepts. Our initial results suggest that artifact-centric RE can be applied to managing legal domain knowledge and engineering-legal coordination. Conclusions: The focus groups that served as a basis for building our model and the results from the expert validation both strengthen our confidence that we already provide a valuable basis for systematically integrating legal concepts into RE. This overcomes contemporary challenges to regulatory RE and serves as a basis for exposure to critical discussions in the community before continuing with the development of tool-supported extensions and large-scale empirical evaluations in practice.
Participatory citizen platforms are innovative solutions to digitally better engage citizens in policy-making and deliberative democracy in general. Although these platforms have been used also in an engineering context, thus far, there is no existing work for connecting the platforms to requirements engineering. The present paper fills this notable gap. In addition to discussing the platforms in conjunction with requirements engineering, the paper elaborates potential advantages and disadvantages, thus paving the way for a future pilot study in a software engineering context. With these engineering tenets, the paper also contributes to the research of large socio-technical software systems in a public sector context, including their implementation and governance.
Mehil B Shah, Mohammad Masudur Rahman, Foutse Khomh
Deep learning (DL) techniques have achieved significant success in various software engineering tasks (e.g., code completion by Copilot). However, DL systems are prone to bugs from many sources, including training data. Existing literature suggests that bugs in training data are highly prevalent, but little research has focused on understanding their impacts on the models used in software engineering tasks. In this paper, we address this research gap through a comprehensive empirical investigation focused on three types of data prevalent in software engineering tasks: code-based, text-based, and metric-based. Using state-of-the-art baselines, we compare the models trained on clean datasets with those trained on datasets with quality issues and without proper preprocessing. By analysing the gradients, weights, and biases from neural networks under training, we identify the symptoms of data quality and preprocessing issues. Our analysis reveals that quality issues in code data cause biased learning and gradient instability, whereas problems in text data lead to overfitting and poor generalisation of models. On the other hand, quality issues in metric data result in exploding gradients and model overfitting, and inadequate preprocessing exacerbates these effects across all three data types. Finally, we demonstrate the validity and generalizability of our findings using six new datasets. Our research provides a better understanding of the impact and symptoms of data bugs in software engineering datasets. Practitioners and researchers can leverage these findings to develop better monitoring systems and data-cleaning methods to help detect and resolve data bugs in deep learning systems.