Engineering Sciences

The work proposes a compact underwater acoustic classification framework combining multi-representation feature engineering, temporal statistical pooling, and compact convolutional architectures designed for acoustic time-frequency and cochlear representations; it first evaluates lightweight classifiers and CNNs on the ShipsEar dataset (a two-layer CNN reaches 0.9918 macro F1 and an RBF-SVM reaches 0.9883), but source-recording provenance cannot be reconstructed, preventing verification of recording-independent generalisation, so it then evaluates on the DeepShip dataset using recording-level partitioning before segmentation, where a 157K-parameter compact CNN achieves a test macro F1 of 0.7226 while an 11.

Presented as a research roadmap, the work surveys the current relationship between search-based software engineering (SBSE), a field active for about 25 years, and AI foundation models (FMs) such as large language models, analyzing three core aspects—using FMs to enhance SBSE, applying SBSE to advance FMs, and exploring their integration—while identifying open challenges and potential research directions and envisioning the future of SBSE in the era of FMs.

MOOSEnger is a modeling-and-simulation AI agent framework for the Multiphysics Object-Oriented Simulation Environment (MOOSE) ecosystem, whose simulation-aware harness combines an interchangeable reasoning model with grounded domain knowledge retrieval, HIT-aware parsing, syntax metadata, language-server diagnostics, revision-controlled authoring, and local or MCP-backed validation and execution in a generate-check-repair-run workflow; across 200 prompts spanning eight simulation families it raises executable success from 10/200 (5%) to 179/200 (89.5%) with GPT 5.2 API and from 0/200 to 153/200 (76.

Decoupling the ASTEC vessel physics (the CESAR-ICARE coupling), this work builds a surrogate that uses an autoencoder for dimensionality reduction and a neural ODE to advance time in latent space, training one model each on station blackout and loss-of-coolant accident data; it predicts about 80 scalar and field variables simultaneously, rolls out stably for 10k to 50k time steps (about 4 to 40 hours), compresses 1913 degrees of freedom to 6 latent dimensions (about 332x), and produces the full spatio-temporal prediction in under a minute on both CPU and GPU, with LOCA mean times of about 26.5 s on CPU and 18.9 s on GPU versus 16879.8 s for ASTEC's ICARE module alone, roughly a 640x speedup.

Addressing the fact that cooperative perception lets connected vehicles share intermediate neural features while V2X links carry far less than a modern detector produces and existing work reports transmitted bytes without their energy cost, this work decides what to share under a single hard ego-side communication budget: an ensemble of gradient-boosted trees and a multilayer perceptron predicts, from metadata available before any feature is transmitted, how many objects a candidate block would add to what the ego alone detects, and a greedy knapsack allocates the budget across helpers, spatial blocks and numerical fidelity (fp16/int8/int4); on the OPV2V benchmark the allocator reaches 99% of full-fusion AP@0.5 while transmitting 20.0 kB per frame instead of 282.

The work presents pm4aa, a pipeline that extracts object-centric event logs from GitHub repositories via PyStack't, maps commits to SE tasks with a Conventional Commits regular expression, partitions 589 users into eight roles with a priority-ordered rule classifier, applies object-centric, imperative (BPMN), and declarative (DECLARE) process mining per role, has an LLM generate process descriptions, and synthesizes a LangGraph multi-agent application through IBM BOB; on the Commitizen project (November 2017 to November 2025, 21,488 events, 4,813 objects) it produced five role agents, three smoke tests routed correctly, and a ten-participant user study rated knowledge schema, operational clarity, and human engagement at a median of 4, accountability at 3, and autonomy at only 2.

This study develops the P2F method, a node-assigned hybrid framework that couples a parameterized Node-Assigned physics-informed neural network (NA-PINN) with a finite difference method (FDM) solver: the parameterized NA-PINN takes the water-level difference, initial velocity, and time t as inputs and learns a solution manifold so that a single trained network serves as a data-free surrogate for the momentum conservation equation across all flow paths, while the FDM solver advances the mass conservation equation at each time step to ensure exact discrete mass conservation; verification on a six-tank gravity-driven draining scenario yields a water level mean absolute error of 7.85×10⁻⁵ m and a velocity mean absolute error of 3.21×10⁻³ m/s under the nominal condition with Δt = 1.

The work proposes a multi-layer learning architecture for optimizing power distribution between electric vehicles and microgrids, comprising a prediction layer, a coordination layer, and a real-time control layer: the prediction layer forecasts demand loads, available renewable energy generation rates, and vehicle availability; the coordination layer solves a constrained optimization problem to allocate energy resources at multiple charging nodes; and the real-time control layer enforces feasibility constraints while compensating for forecast inaccuracies via adaptive control and reinforcement learning; simulation studies based on representative microgrid use cases indicate improved use of renewable energy resources, reduced peak demand, and increased economic returns relative to tradition