Algorithms

Parallax images often suffer from insufficient and unstable point features, making accurate alignment in overlapping regions extremely challenging. To address this issue, this paper proposes a robust image stitching framework based on content-preserving warping and quality-aware seam-cutting. The framework first estimates a global homography by combining point and line features to achieve reliable initial alignment. Subsequently, an energy function composed of alignment, distortion, and saliency terms is optimized to reduce geometric deformation while preserving image structures. After obtaining an initial seam using conventional seam-cutting, a structural similarity (SSIM)-based quality evaluation mechanism is introduced to identify misaligned regions and extract corresponding patches. To refine local alignment, the scale-invariant feature transform flow (SIFT flow) is employed to estimate dense correspondences, and a sigmoid-based smoothing constraint is adopted to avoid boundary artifacts. Finally, the optimized patches are integrated into the final result using a revised seam-cutting strategy with enhanced data penalty terms. Experimental results demonstrate that the proposed method achieves competitive performance, with favorable artifact suppression and structural preservation compared with mainstream stitching methods.

Agentic coding systems increasingly automate repository inspection, tool use, code generation, testing, and repair, but the human commonly remains responsible for decomposing the task, directing workflow progression, and judging completion. This limits their use in specialised domains where users understand the problem but not the development procedure required to solve it. This study introduces inversion of initiative (IoI) as a human–Agentic AI interaction paradigm in which procedural and modelling initiative shifts from the user to a procedure-aware Agentic AI, while human authority over domain meaning, consequential assumptions, validity boundaries, and progression is retained. The paradigm is instantiated for agent-based simulation development. Starting from an ordinary-language account of the problem, intended use, system, and investigative questions, the Agentic AI constructs and evaluates a structured natural-language simulation specification, resolves modelling and technical gaps through encoded skills, documentation, data, and authorised retrieval, and invokes bounded coding and assurance tools without requiring the user to orchestrate development. A browser platform operationalises IoI through an eight-stage deterministic workflow, versioned artefacts, readiness gates, traceability, semantic checks, controlled returns, and remote execution. In one end-to-end session, the platform transformed a neighbourhood energy-balancing brief containing neither an agent-based model nor a development plan into an AI-authored neighbourhood energy-balancing simulation specification, a verified configurable simulation package, and executed scenario comparison. The results support the feasibility of IoI in this specific case and illustrate an interaction pattern for agentic systems in which procedural and modelling initiative can reside with the AI while accountable domain authority remains human.

Residential water end-use monitoring is rapidly advancing from passive metering to AI-driven digital telemetry; however, systemic operational trade-offs remain poorly quantified. This systematic review synthesizes N = 100 peer-reviewed studies to critically evaluate algorithm architectures, sensing topologies, and deployment constraints in smart water disaggregation. Quantitative analysis reveals an overwhelming prevalence of non-intrusive paradigms ($89.0\%$, $N=89$), with $45.0\%$ ($N=45$) specifically targeting multi-fixture concurrent usage events. While high-frequency setups (≥$1\mathrm{Hz}$) utilizing deep learning architectures (e.g., Seq2Point, 1D-CNNs) achieve high disaggregation accuracy ($F_1>0.88$) in laboratory environments, performance degrades severely under commercial Automated Metering Infrastructure (AMI) supplying coarse interval data. Four critical technical bottlenecks hindering utility-scale scalability are systematized: (1) severe temporal resolution mismatches between high-frequency training models and utility-scale AMI, (2) non-linear hydraulic superposition during simultaneous fixture activations, (3) domain transferability loss across heterogeneous plumbing topologies, and (4) memory and power constraints hindering edge TinyML execution (≤$512\mathrm{KB}$ SRAM). To address these challenges, an empirically validated three-layer conceptual analytics framework is integrated alongside five evidence-based future research directions, providing a structured roadmap to transition smart water analytics from experimental setups to robust, utility-scale deployments.