Hybrid hydro–solar microgrids offer a practical electrification option for remote and weak-grid communities by combining run-of-river hydropower with photovoltaic generation. However, their performance depends strongly on coordinated decisions across three layers: (i) system sizing and architecture, (ii) turbine selection and rating under variable river flow, and (iii) operational energy dispatch under time-varying solar resource and demand. This paper develops an optimization-driven planning framework for a run-of-river hydro–PV microgrid that co-optimizes component capacities and turbine-related design choices while enforcing time-series operational feasibility. Physics-based component models translate river discharge into hydroelectric output via turbine efficiency characteristics and operating limits, and compute PV generation and storage trajectories under dispatch and state-of-charge constraints. The planning problem is formulated as a multi-objective optimization that quantifies trade-offs among life-cycle cost, supply reliability (e.g., unmet-load metrics), and sustainability indicators (e.g., diesel-free operation or emissions when backup generation is present). A Pareto-optimal set of designs is obtained using a population-based multi-objective algorithm, and representative knee-point (balanced) solutions are selected to illustrate how turbine choice and dispatch strategy interact with seasonal hydrology and solar variability. The proposed approach supports transparent and robust design decisions for hybrid hydro–solar microgrids.

Vegetation-related high-impedance faults (VHIFs) are difficult to detect because their low and intermittent currents often evade conventional protection and phasor-based monitoring. These events arise on overhead feeders when conductors contact vegetation, producing weak, sporadic arcs whose diagnostic signatures concentrate in the high-frequency (HF) band. This study develops a waveform-derived severity mapping and placement framework that converts HF arcing indicators into bus-level risk indices and then into a candidate set of diagnostic test points. Using the staged Vegetation High-Impedance Fault (VeHIF) dataset, wavelet- and spectral-based descriptors are classified with an RBF-SVM to generate VHIF severity maps, achieving 96.1% out-of-fold accuracy with near-symmetric performance across risk classes. Test-point placement is formulated as a bi-objective problem that maximizes normalized severity coverage while minimizing adjacency-based redundancy. NSGA-II is used to explore the coverage–redundancy trade-off and identify a Pareto knee solution, while a tuned Binary Particle Swarm Optimization (BPSO) stage reproduces this compromise as a single deployable layout. MOEA/D serves as a decomposition-based benchmark to assess algorithmic stability. On the IEEE 14-bus system, the selected knee configuration achieves 80.32% VHIF-severity coverage with a redundancy ratio of 0.400 using eight test points. The BPSO stage reproduces the same objective balance with significantly lower runtime, supporting rapid scenario re-evaluation. The framework provides a reproducible pathway from HF waveform evidence to risk-informed bus selection. It is implemented as OPTIVE HIF, a web-based platform that operationalizes classification, spatial severity mapping, and optimization for the IEEE-14 demonstration. These results illustrate how HF-driven severity modeling can support structured, risk-aware planning for VHIF monitoring in distribution networks.

This study presents the design, embedded implementation, and screening-level experimental terrain-performance evaluation of an RHex-inspired hexapod robot using a fixed encoder-assisted alternating-tripod state-machine gait. The work aims to provide an experimentally grounded assessment of how terrain properties and practical leg-thickness variation influence the locomotion of a fabricated low-complexity legged platform. A 2 × 2 × 2 full-factorial screening design was adopted to evaluate terrain roughness, terrain compliance, and leg thickness. Four terrain conditions were tested: concrete, rocky terrain, foam mats, and grass, corresponding to smooth–rigid, rough–rigid, smooth–soft, and rough–soft surfaces, respectively. Each treatment combination was evaluated in two replicate runs using final forward displacement, lateral displacement, absolute displacement, and peak current as the response variables. The full-factorial analysis showed that terrain roughness had the clearest significant effect on forward displacement and absolute displacement, while terrain compliance significantly affected absolute displacement and showed observable trends in lateral displacement and peak current. Leg thickness did not produce a statistically significant main effect within the tested 2.5 mm and 5.0 mm configurations, fixed gait, and terrain set. The findings indicate that, for this platform and experimental scope, terrain roughness and compliance affected locomotion more strongly than the tested morphology variation. The study contributes a reproducible baseline workflow for terrain-performance evaluation in low-complexity legged robots and identifies directions for future work involving stronger replication, quantified terrain characterization, improved energy measurement, and closed-loop terrain-adaptive control.

Natural and anthropogenic disasters are rising in frequency and severity, and reactive, single-technology response models are now the principal bottleneck in safeguarding planetary health. This article develops Hyperautomated Hazard Mitigation (HHM), in which robots, unmanned aerial vehicles (UAVs), and artificial intelligence (AI) services operate as one integrated, self-optimizing system rather than as isolated tools. Hazard mitigation is treated as a specific resilience behavior of a multi-agent engineering system, building on established proactive-resilience theory rather than presenting that framing as new. This framework-oriented review synthesizes evidence and proposes an architecture. It reports no new experimental validation. It maps hazard-mitigation functions onto the six layers of the Reference Architectural Model Industrie 4.0 (RAMI 4.0) with the standards and interfaces that realize each layer; classifies robotic and UAV functions across natural and anthropogenic hazards using one set of four capability classes; expresses ethical and regulatory requirements as quantified engineering constraints; operationalizes health- centered evaluation metrics; and appraises the maturity of the evidence in each hazard domain. It concludes that the architecture is tractable with existing standards, that the actuation stage is the least mature and least evaluated, and that the decisive gap is evaluative: the field has no shared benchmark and rarely reports health outcomes.