Abstract This study develops a novel metaheuristic algorithm that is motivated by the behavior of jellyfish in the ocean and is called artificial Jellyfish Search (JS) optimizer. The simulation of the search behavior of jellyfish involves their following the ocean current, their motions inside a jellyfish swarm (active motions and passive motions), a time control mechanism for switching among these movements, and their convergences into jellyfish bloom. JS optimizer is tested using a comprehensive set of mathematical benchmark functions and applied to a series of structural engineering problems. Fifty small/average-scale and twenty-five large-scale functions involving various dimensions were used to validate JS optimizer, which was compared with ten well-known metaheuristic algorithms. JS optimizer was found to outperform those algorithms in solving mathematical benchmark functions. The JS algorithm was then used to solve structural optimization problems, including 25-bar tower design, 52-bar tower design and 582-bar tower design problems. In those cases, JS not only performed best but also required the fewest evaluations of objective functions. Therefore, JS is potentially an excellent metaheuristic algorithm for solving optimization problems.
Reinforced concrete (RC) beams strengthened with carbon-fabric-reinforced cementitious matrix (CFRCM) systems have shown potential for restoring flexural performance, yet their effectiveness under different corrosion levels remains insufficiently understood. This study presents a numerical investigation of the flexural behaviour of simply supported RC beams externally strengthened with CFRCM plates. Refined finite element models (FEMs) were developed by explicitly incorporating the steel–concrete bond-slip behaviour, the carbon fabric (CF) mesh–cementitious matrix (CM) interface, and the CFRCM–concrete substrate interaction and were validated against experimental results in terms of failure modes, load–deflection responses, and flexural capacities. A parametric study was then conducted to examine the effects of CFRCM layer number, steel corrosion level, and longitudinal reinforcement ratio. The results indicate that the baseline flexural capacity can be fully restored only when the corrosion level remains below approximately 15%; beyond this threshold, none of the CFRCM configurations achieved full recovery. The influence of the reinforcement ratio was found to depend on corrosion severity, while increasing CFRCM layers enhanced flexural performance but exhibited saturation effects for thicker configurations. In addition, corrosion level and CFRCM thickness jointly influenced the failure mode. Comparisons with design predictions show that bilinear CFRCM constitutive models are conservative, whereas existing FRP-based design codes provide closer agreement with numerical and experimental results.
Daniel Rodriguez-Cardenas, Xiaochang Li, Marcos Macedo
et al.
Large language models for code are advancing fast, yet our ability to evaluate them lags behind. Current benchmarks focus on narrow tasks and single metrics, which hide critical gaps in robustness, interpretability, fairness, efficiency, and real-world usability. They also suffer from inconsistent data engineering practices, limited software engineering context, and widespread contamination issues. To understand these problems and chart a path forward, we combined an in-depth survey of existing benchmarks with insights gathered from a dedicated community workshop. We identified three core barriers to reliable evaluation: the absence of software-engineering-rich datasets, overreliance on ML-centric metrics, and the lack of standardized, reproducible data pipelines. Building on these findings, we introduce BEHELM, a holistic benchmarking infrastructure that unifies software-scenario specification with multi-metric evaluation. BEHELM provides a structured way to assess models across tasks, languages, input and output granularities, and key quality dimensions. Our goal is to reduce the overhead currently required to construct benchmarks while enabling a fair, realistic, and future-proof assessment of LLMs in software engineering.
Paloma Guenes, Rafael Tomaz, Maria Teresa Baldassarre
et al.
The Impostor Phenomenon (IP) impacts a significant portion of the Software Engineering workforce, yet it is often viewed primarily through an internal individual lens. In this position paper, we propose framing the prevalence of IP as a form of Human Debt and discuss the relation with the ICSE2026 Pre Survey on the Future of Software Engineering results. Similar to technical debt, which arises when short-term goals are prioritized over long-term structural integrity, Human Debt accumulates due to gaps in psychological safety and inclusive support within socio-technical ecosystems. We observe that this debt is not distributed equally, it weighs heavier on underrepresented engineers and researchers, who face compounded challenges within traditional hierarchical structures and academic environments. We propose cultural refactoring, transparency and active maintenance through allyship, suggesting that leaders and institutions must address the environmental factors that exacerbate these feelings, ensuring a sustainable ecosystem for all professionals.
Abstract The modulation of anticyclonic subsurface‐intensified mode‐water eddies (MWEs) on the oceanic physical and biological responses to tropical cyclones (TCs) is investigated using satellite measurements, in situ observations and numerical model outputs. Extreme cooling of the surface (4.2°C) and mixed‐layer (2.3°C) is observed in a MWE, which can be remarkably stronger than those in adjacent cyclonic eddy and non‐eddy environments. The special thermodynamic structure above the lens of MWEs, which would favor the TC‐induced entrainment more efficiently, facilitates the elevation of substantial subsurface cold water. It also leads to increased mixed‐layer salinity and deepening of the mixed‐layer. Additionally, variations in nitrate and chlorophyll‐a concentrations appear to be depressed and exhibit intricate multi‐layer patterns due to TC‐induced and MWE‐influenced vertical processes. This study provides novel insights into the interactions between TCs and subsurface‐intensified eddies.
Johnathan Daniel Maxey, Neil D. Hartstein, Dane Dickinson
et al.
Abstract Aquaculture’s contribution to global N2O emissions is poorly constrained and often reliant on supply chain/industrial emissions/life-cycle analyses which generalise system responses to farm-derived inputs and contain few examples of direct measurements made in situ. Among the studies that do report aquaculture associated N2O emissions the focus has been on pond culture and wetlands systems rather than open marine systems. Our study examined the effects of open system aquaculture culture on water column N2O cycling in two hydrodynamically contrasting southern hemisphere systems: the heavily stratified Macquarie Harbour, Tasmania, Australia and the semi-enclosed but well-mixed Big Glory Bay, New Zealand. Significant, but localised, N2O undersaturation was observed under the active salmon farm in the heavily stratified Macquarie Harbour during the peak feeding season, but not under fallowed salmon farms or the non-farmed areas. This was observed in a low-oxygen but not anoxic water column. Water column N2O was either in equilibrium with the atmosphere or supersaturated in all other instances. In Big Glory Bay N2O undersaturation was observed during winter and spring sampling surveys that generally persisted across the bay and resulted in removal of atmospheric N2O. The specific mechanisms of N2O loss are still uncertain but is likely driven by a combination of particle associated denitrification activity in farm waste plumes, denitrification/DNRA in sediments and on the detritus covered mussel shells and lines. Overall, this study demonstrates that industry impacts to N2O cycling can include loss dynamics which have previously been unreported. Therefore, global estimates of N2O emissions from aquaculture may be significantly overestimated.
The overexploitation of wild populations for the marine ornamental trade necessitates optimized captive breeding, particularly for iconic species like the clownfish <i>Amphiprion ocellaris</i>. This study investigated the social behavior and cognitive abilities of juvenile clownfish in relation to artificial rearing practices. Using modified three-tank tests, we assessed social preference and cognition ability in two size groups: Small-bodied Group (SG: 2.0–2.5 cm) and Large-bodied Group (LG: 3.5–4 cm). The results indicated that clownfish have the following: (a) Strong Social Preference: Both SG and LG exhibited significant preference for areas near conspecifics (SPI > 0), with SG showing significantly higher SPI values than LG. (b) Developmental Stage Differences: SG demonstrated a stronger tendency to cluster tightly near conspecifics. LG showed wider exploration patterns and greater movement. (c) Cognition Ability: SG showed renewed interest towards a novel fish after habituation to a familiar fish, while LG displayed a stronger preference for the familiar fish. These findings suggest that clownfish juveniles possess advanced sociality and basic cognition ability. Furthermore, the observed shift in social interaction preference with developmental stages informs optimal timing for grading practices to minimize artificial rearing stress. This study provides some behavioral insights for optimizing large-scale artificial rearing protocols for clownfish, reducing pressure on wild populations.
Yaoteng Zhang, Shuaipeng Wang, Yanlong Chen
et al.
To maintain marine ecosystem health, effective algae monitoring is essential. Traditional threshold-based methods and standard machine learning techniques often fall short in accurately and automatically distinguishing algae types. This study presents Algae-Mamba, an advanced network for algae extraction that builds upon the visual state-space (VSS) model. The Algae-Mamba unified VSS model and the Kolmogorov–Arnold network proposed the Kolmogorov–Arnold visual state space (KVSS) model. KVSS block combines VSS for comprehensive global feature extraction with a small-kernel convolution module to capture local spatial and channel-specific information, supporting multiscale data processing and improving model generalization. The KVSS represents high-dimensional features using orthogonal polynomial combinations through Gram polynomials and leverages an attention mechanism to index interactions between target algae and their features, enabling the model to learn distinct characteristics of sargassum and ulva effectively and enhance extraction precision. To address the common misclassification between sargassum and ulva under limited spectral data, Algae-Mamba incorporates the normalized difference water index (NDWI) to enhance semantic richness. Furthermore, the model addresses class imbalances by employing a hybrid cross-entropy and Lovász-Softmax loss function, ensuring balanced and robust training. Unlike other methods that depend on extensive spectral information, Algae-Mamba achieves precise differentiation of sargassum and ulva with just 4-band spectral imagery, offering a powerful tool for monitoring marine ecological security. Testing on the GF-1 algae dataset demonstrates that Algae-Mamba surpasses other deep learning approaches in accurately extracting sargassum and ulva.
Shear wave velocity (V<sub>s</sub>) is an important soil parameter to be known for earthquake-resistant structural design and an important parameter for determining the dynamic properties of soils such as modulus of elasticity and shear modulus. Different V<sub>s</sub> measurement methods are available. However, these methods, which are costly and labor intensive, have led to the search for new methods for determining the V<sub>s</sub>. This study aims to predict shear wave velocity (V<sub>s</sub> (m/s)) using depth (m), cone resistance (q<sub>c</sub>) (MPa), sleeve friction (f<sub>s</sub>) (kPa), pore water pressure (u<sub>2</sub>) (kPa), N, and unit weight (kN/m<sup>3</sup>). Since shear wave velocity varies with depth, regression studies were performed at depths up to 30 m in this study. The dataset used in this study is an open-source dataset, and the soil data are from the Taipei Basin. This dataset was extracted, and a 494-line dataset was created. In this study, using HyperNetExplorer 2024V1, V<sub>s</sub> prediction based on depth (m), cone resistance (q<sub>c</sub>) (MPa), shell friction (f<sub>s</sub>), pore water pressure (u<sub>2</sub>) (kPa), N, and unit weight (kN/m<sup>3</sup>) values could be performed with satisfactory results (R<sup>2</sup> = 0.78, MSE = 596.43). Satisfactory results were obtained in this study, in which Explainable Artificial Intelligence (XAI) models were also used.
Estimation and analyses of significant wave heights (SWHs) are crucial to climate research, ocean engineering and other applications, with satellite retrieval serving as a fundamental approach. However, few studies attempt to extrapolate SWH models across buoy grids to retrieve ungauged-grid SWHs from multiple altimeters at macroscales, or examine variations of extreme SWHs in relation to climate oscillations, particularly in the Canadian context. To fill these gaps, we develop a macroscale spatially-extrapolative ensemble wave-height retrieval and analysis (MEERA) method to retrieve SWHs from multi-mission satellite altimetry and reveal tempo-spatial characteristics of SWHs means and extremes as well as their variations with climate oscillations. The method is applied across all Canadian waters. According to modeling results, MEERA significantly enhances consistency and accuracy of retrieved SWHs (especially in coastal areas), e.g., reducing biases of conventional methods by over 98%. From 1985 to 2020, waves were strongly seasonal and regional, which drop from winter (1.45 m) to summer (1.17 m) and tend to decline northward. SWHs tend to decrease in mid-eastern regions (e.g., Hudson Bay, Davis Strait and Gulf of St Lawrence) and increase in Canadian Atlantic, Pacific, and Arctic. Across all Canadian waters, climate indices regarding precipitation, e.g., the NBRA (Northeast Brazil Rainfall Anomaly) index, pose the strongest impacts on extreme SWHs compared with others. In Pacific and Atlantic, spatial patterns of winter SWH extremes are associated with negative NAO (North Atlantic Oscillation). El Niño might increase SWHs extremes over the Pacific and Arctic, while decreasing them over mid-eastern regions. This study advances macroscale SWH estimation and analysis, enhancing the understanding of SWH characteristics and their variations across Canada under climate change.
Nasir U. Eisty, Jeffrey C. Carver, Johanna Cohoon
et al.
In the evolving landscape of scientific and scholarly research, effective collaboration between Research Software Engineers (RSEs) and Software Engineering Researchers (SERs) is pivotal for advancing innovation and ensuring the integrity of computational methodologies. This paper presents ten strategic guidelines aimed at fostering productive partnerships between these two distinct yet complementary communities. The guidelines emphasize the importance of recognizing and respecting the cultural and operational differences between RSEs and SERs, proactively initiating and nurturing collaborations, and engaging within each other's professional environments. They advocate for identifying shared challenges, maintaining openness to emerging problems, ensuring mutual benefits, and serving as advocates for one another. Additionally, the guidelines highlight the necessity of vigilance in monitoring collaboration dynamics, securing institutional support, and defining clear, shared objectives. By adhering to these principles, RSEs and SERs can build synergistic relationships that enhance the quality and impact of research outcomes.
This paper explores how generative AI can help automate and improve key steps in systems engineering. It examines AI's ability to analyze system requirements based on INCOSE's "good requirement" criteria, identifying well-formed and poorly written requirements. The AI does not just classify requirements but also explains why some do not meet the standards. By comparing AI assessments with those of experienced engineers, the study evaluates the accuracy and reliability of AI in identifying quality issues. Additionally, it explores AI's ability to classify functional and non-functional requirements and generate test specifications based on these classifications. Through both quantitative and qualitative analysis, the research aims to assess AI's potential to streamline engineering processes and improve learning outcomes. It also highlights the challenges and limitations of AI, ensuring its safe and ethical use in professional and academic settings.
The study of behavioral and social dimensions of software engineering (SE) tasks characterizes behavioral software engineering (BSE);however, the increasing significance of human-AI collaboration (HAIC) brings new directions in BSE by presenting new challenges and opportunities. This PhD research focuses on decision-making (DM) for SE tasks and collaboration within human-AI teams, aiming to promote responsible software engineering through a cognitive partnership between humans and AI. The goal of the research is to identify the challenges and nuances in HAIC from a cognitive perspective, design and optimize collaboration/partnership (human-AI team) that enhance collective intelligence and promote better, responsible DM in SE through human-centered approaches. The research addresses HAIC and its impact on individual, team, and organizational level aspects of BSE.
The design of effective programming languages, libraries, frameworks, tools, and platforms for data engineering strongly depends on their ease and correctness of use. Anyone who ignores that it is humans who use these tools risks building tools that are useless, or worse, harmful. To ensure our data engineering tools are based on solid foundations, we performed a systematic review of common programming mistakes in data engineering. We focus on programming beginners (students) by analyzing both the limited literature specific to data engineering mistakes and general programming mistakes in languages commonly used in data engineering (Python, SQL, Java). Through analysis of 21 publications spanning from 2003 to 2024, we synthesized these complementary sources into a comprehensive classification that captures both general programming challenges and domain-specific data engineering mistakes. This classification provides an empirical foundation for future tool development and educational strategies. We believe our systematic categorization will help researchers, practitioners, and educators better understand and address the challenges faced by novice data engineers.