Search Results (1,095)

Operational marine buoys provide essential in situ wind observations, but platform attitude variations induced by wind–wave forcing can affect wind-speed measurement quality. Existing high-frequency motion-correction methods are difficult to apply to routine buoy archives that contain only averaged outputs. This study analyzed 6964 quality-screened half-hourly observations from an operational 10 m buoy in the Changjiang Estuary (7 July–1 December 2025) and developed a Motion Impact Index (MII)-based quality classification framework for buoy wind-speed measurements under attitude and sea-state variations. The composite tilt angle was right-skewed (mean 10.68°, 95th percentile 23.09°) and significantly correlated with significant wave height ($r=0.388$, $p<0.001$). Theoretical cosine-response analysis indicated a geometric projection effect of −1.7% at the mean tilt and about −8% at the 95th percentile, whereas wind-direction dispersion showed no significant attitude dependence under$U\ge 5\mathrm{m}/\mathrm{s}$. The MII was defined as $MII={\theta }_{tilt}\times (1+k\bullet {\displaystyle \frac{H_s}{H_{ref}}})$, with $k$ = 1.0 adopted as an engineering default, and four quality classes were established using the 50th, 80th, and 95th percentiles. Time-split testing, parameter-sensitivity analysis, and bootstrap resampling indicated that the thresholds were statistically stable. When stratified by ERA5 wind speed, the buoy–ERA5 bias increased systematically across the four classes, supporting their interpretation as progressively different measurement conditions. The numerical MII thresholds reported here are specific to the platform type and deployment site; when applied to other buoy designs or sea areas, the thresholds should be recalibrated from local data. Full article

Egocentric multi-view image analysis refers to the processing of utilizing synchronized video streams captured from multiple wearable cameras worn on the head or body, providing complementary first-person perspectives of dynamic, real-world interactions. Unlike single-view egocentric vision, which may suffer from severe occlusions, motion blur, and limited field-of-view or traditional fixed-camera multi-view setups (assuming static geometry and controlled environments), egocentric multi-view systems leverage body-worn rigs to enable a more robust and flexible 3D understanding in open-world, mobile scenarios. In this work, we present a systematic survey of advancements in cross-view feature fusion, geometric consistency enforcement, open-world detection, human–object interaction (HOI) modeling, action segmentation, 3D reconstruction, and novel-view synthesis specifically tailored to wearable multi-camera platforms. Key datasets released between 2024 and 2026—including HOT3D (833 min of synchronized multi-view hand/object interactions from Project Aria and Quest 3), MultiEgo (first multi-egocentric dataset for 4D social scene reconstruction), and Ego-1K (large-scale 12-camera rig for dynamic 3D video synthesis) are thoroughly examined alongside an analysis of integrations with large language models (LLMs) and vision–language models that drive performance gains, typically in the 15–30% range over single-view baselines in hand tracking, HOI recognition, and reconstruction fidelity, although we show through a consolidated meta-analysis that this gain is task-dependent: larger for geometry-bottlenecked tasks such as in-hand object lifting, and smaller, method-dependent, or occasionally negative for semantic-recognition tasks such as keystep recognition under naive view fusion. These methods cover work in multi-view stereo, cross-view learning, and novel-view synthesis while addressing several real-time wearable constraints. Practical applications such as immersive Augmented Reality/Virtual Reality (AR/VR), assistive robotics, and healthcare monitoring are also discussed together with the challenges in motion calibration, benchmark diversity, and edge deployment ability. Thus, in this review, we attempt to fill a critical gap by focusing exclusively on wearable multi-view systems in an open-world setting, synthesizing the latest literature to chart future directions toward more embodied and continual learning agents. Full article

High renewable penetration and large-scale green hydrogen production are accelerating the formation of the new-type power system (NTPS), in which electrical dispatch, electrolysis, hydrogen storage, fuel-cell reconversion, and flexible demand must be coordinated under nonlinear network physics and uncertain renewable, load, and hydrogen-demand trajectories. This study develops a physics-informed distributionally robust multi-agent reinforcement learning (PI-DRO-MARL) framework for coordinated NTPS operation with integrated electricity–hydrogen coupling. The operational objective is to minimize worst-case expected operating cost, including generation and grid-exchange cost, electrolysis and hydrogen-delivery cost, storage degradation, renewable curtailment, and load- or hydrogen-shedding penalties, while satisfying AC power-flow balance, voltage limits, line-loading limits, ramping limits, battery state-of-charge constraints, hydrogen-storage dynamics, and electrolysis/fuel-cell conversion constraints. The framework embeds physics-informed residuals and projection operators into a centralized-training decentralized-execution architecture; represents renewable, electrical-load, hydrogen-demand, and price uncertainty through statistically calibrated Wasserstein ambiguity sets; and trains agents with robust value estimation and feasibility-aware action correction. Validation is conducted on a modified IEEE 33-bus distribution network coupled with a 12-node hydrogen system, with additional scalability checks on modified IEEE 69-bus and IEEE 123-node reference systems. Across ten random seeds, the primary case shows an operating cost of USD 8850 with a 95% confidence interval of USD 8770–8940, a mean constraint-violation rate of 0.37%, and a shifted-scenario cost increase of 12.6%, outperforming deterministic optimization, stochastic programming, standard reinforcement learning (RL), proximal policy optimization (PPO), soft actor–critic (SAC), multi-agent deep deterministic policy gradient (MADDPG), constrained RL, safe RL, and robust RL baselines. Ablation, Wasserstein-radius, time-step, and stress-test analyses further show that distributional robustness, physics-informed projection, and multi-agent coordination provide distinct and complementary benefits. The results support PI-DRO-MARL as a simulation-validated architecture for real-time, uncertainty-aware NTPS dispatch, while field deployment still requires digital-twin calibration, hardware-in-the-loop testing, and site-specific operational validation. Full article

Hydrogen Fuel Cell Electric Buses (FCEBs) are a promising solution for decarbonizing public transport. Their operational feasibility in developing countries depends on hydrogen-supply costs and infrastructure readiness. The Ministry of Energy has reported a 400% increase in imported natural-gas costs due to current geopolitical tensions between the United States and Iran, exposing the strategic vulnerability of relying on imported diesel for public transport. This study assesses the operational hydrogen demand, fuel-cost implications, and avoided diesel tailpipe carbon dioxide (CO₂) emissions associated with gradually integrating FCEBs into Jordan’s Bus Rapid Transit (BRT) project. The baseline system consists of 64 diesel buses covering about 11 million kilometers annually, consuming around 3 million liters of diesel and emitting roughly 8108.3 tons of CO₂. Fleet transition scenarios are assessed for 2030, 2035, and 2040, with FCEB-integration ratios of 10%, 15%, and 25%. Under the reference assumption of equivalent-duty replacement, avoided diesel tailpipe CO₂ emissions increase with the diesel service displaced by FCEBs, reaching 25.7% at the highest penetration level. At the $6/kg reference hydrogen-price input, FCEB integration results in higher operational fuel costs than diesel-only operation. Under the constant diesel-price reference, mixed-fleet operation becomes cost-competitive at hydrogen prices of $1/kg and $2/kg, with operational fuel-cost savings of up to 16.97%. Within the operational fuel-cost boundary of this study, mixed-fleet competitiveness is influenced by both the assumed hydrogen fuel price and diesel-price trajectory. The results support gradual deployment, subject to the greenhouse-gas intensity of the hydrogen-production and delivery pathway and the readiness of hydrogen supply and depot infrastructure. Full article

Platinum group metals (PGM) are essential for clean-energy technologies, including proton exchange membrane (PEM) electrolyzers for hydrogen production and PEM fuel cells for hydrogen use, as well as catalytic, electronic, and advanced industrial applications. However, their supply is exposed to geological concentration, co-product dependence, and market volatility. This study evaluates the medium-term cost-based accessibility of known primary PGM resources using a cumulative availability curve. The analysis combines resource estimates and allocated production-cost data for 61 known PGM-bearing deposits and projects, with costs expressed in 2022 USD per metric ton of combined PGM output. Because PGM deposits differ in ore type, processing route, and co-product setting, the results are interpreted by deposit cluster rather than only by country or aggregate cost threshold. The low-cost portion of the curve is dominated by Ni–Cu sulphide by-product systems, but this cluster represents only 1.87% of the compiled resource base. In contrast, UG2/Merensky/Great Dyke reef-type systems account for 72.95%, and Platreef/Northern Limb and Platreef-type systems account for 19.82%. Thus, most known primary PGM resources occur outside the low-cost by-product segment. Cluster-weighted PGM basket-price benchmarks are used instead of individual metal-price comparisons. Several cluster-level cost ranges fall below or near indicative April 2025 basket-price benchmarks, but these comparisons are not project-level profitability tests. Overall, the cumulative availability curve provides a deposit-cluster-based framework for evaluating known primary PGM availability and informing critical-material policy, recycling strategy, supply-chain planning, hydrogen-technology deployment, and responsible resource development. Full article

Global food demand is projected to rise by approximately 56 percent between 2010 and 2050, intensifying reliance on synthetic nitrogen fertilizer during a time when only about half of all applied nitrogen is recovered by crops, with the remainder split between genuine environmental loss and retention within soil and biomass pools. The fraction that is genuinely lost drives substantial economic costs and contributes disproportionately to global nitrous oxide emissions, a greenhouse gas with a warming potential far exceeding that of carbon dioxide. This review synthesizes recent literature across three interdependent domains including nitrogen use efficiency strategies spanning agronomic, genetic, and microbial approaches, decarbonization pathways for ammonia synthesis ranging from conventional to green production routes, and gas sensing technologies for monitoring ammonia and nitrous oxide emissions in agricultural settings. Rather than treating these domains separately, this review proposes that their effects on overall emissions are complementary and potentially compounding rather than strictly additive. Efficiency improvements reduce the total fertilizer volume subject to production emissions, while cleaner production cannot offset nitrogen loss in the field. The exact extent of any combined benefit depends on the relative proportion of field emissions and production emissions within each farming system. Another important finding is the pronounced asymmetry in monitoring readiness. Ammonia sensing has reached field-deployable maturity for detection and concentration monitoring. In contrast, nitrous oxide sensing remains constrained by unresolved challenges in sensitivity and long-term stability despite the gas’s significant contribution to climate change. This asymmetry limits the verification of mitigation outcomes at farm and regional scales. The review further identifies that intervention effectiveness depends on farm structure in the studied context, that global nitrogen policy remains weighted toward incentivizing use rather than reducing pollution, and that the evidence base surveyed here is geographically uneven. Together, these findings indicate that reconciling rising food production with greenhouse gas reduction targets requires integrated frameworks linking field nitrogen budgets, production emissions, and monitoring capability, alongside policy instruments designed around their interdependence. Full article

Autonomous inspection of ballast water tanks requires three-dimensional (3D) LiDAR-based simultaneous localization and mapping (SLAM) in Global Positioning System (GPS)-denied, geometrically repetitive interiors, where sensing, mapping, and control modules share a limited onboard memory budget. Graph SLAM backends that rely on sparse factorization can incur fill-in, increasing peak memory and limiting deployment on edge computers. The proposed architecture couples a robust hierarchical bundle adjustment frontend with a factorization-free Kaczmarz backend. The frontend combines residual-adaptive weighting, damped and bounded pose updates, soft fallback, local-map compression, and memory-aware keyframe control. The backend stores the whitened Jacobian in compressed sparse row (CSR) format and performs row-wise projections without explicitly forming the normal equations, a Cholesky factor, or a transpose cache. Evaluation was conducted on Norwegian University of Science and Technology (NTNU) Ballast Water Tank missions 1–3, containing 851, 1202, and 1084 LiDAR frames. Following robust local bundle adjustment and verified similarity alignment, translational root-mean-square errors were 0.080, 0.110, and 0.127 m, corresponding to 0.137%, 0.143%, and 0.122% of the reference path lengths; archived baseline ratios ranged from 0.281% to 0.372%. These results support a numerical architecture that combines frontend stabilization, row-wise optimization, and memory-aware policies for resource-constrained marine inspection robots. Full article

Autonomous warehouse drones require detection that is both accurate and spatially selective, detecting only the labels directly in front of the drone and ignoring those on neighbouring racks. Standard YOLOv8n has no built-in awareness of absolute pixel position, and architectural fixes (CoordConv, attention modules) are precluded by the ModalAI VOXL2 TFLite GPU runtime that constrains the deployment platform. We propose Geometric Channel Hijacking (GCH), a data-level technique that collapses the colour camera image to a single greyscale channel and replaces the two remaining input channels with horizontal and vertical positional gradients. We then run a controlled multi-factor ablation across three training regimes (INESC TEC’s Autonomous Systems Laboratory (LSA), Volkswagen Autoeuropa (AE) production, and the LSA + AE combined multi-domain set), six training set sizes from $n=50$ to full, and six independent random seeds per cell, totalling 200 retrained models (150 across the data-size sweep and 50 at full N), on top of the original seed-0 runs, evaluated on three equalised 42-image test sets. At full data, GCH and an architecturally identical Target-Only Annotation (TOA) ablation are empirically equivalent across every metric we tested, showing that target-only annotation alone is sufficient to induce spatial selectivity in unmodified YOLOv8n. At low data, the multi-seed analysis reveals no GCH-favouring difference in mean spatial precision at $p<0.05$ in any of the 18 $(\mathrm{regime}, N)$ cells tested (paired t-test; two cells exhibit a small TOA-favouring gap in the AE regime), but TOA training in the single-domain LSA regime at $N\le 100$ exhibits a stochastic collapse failure mode that affects ∼1 in 6 seeds (cross-seed std of ∼33 percentage points on SpP vs. ∼5.6 pp for GCH), which we also reproduce in AE at $n=100$. GCH eliminates this collapse mode in the LSA regime and provides a ∼6× variance reduction at $n=50$. An ablation with patience = 999 additionally shows that the collapse is recoverable in principle with roughly 2.4× the standard training budget. A quantitative cross-camera test on a $1920 \times 1080$ Arducam 64 MP USB module (a completely different sensor and lens to the IMX412 used during training, no retraining performed, 115 manually annotated frames) reveals a second regime in which GCH beats TOA: cross-camera target recall is $82.5\%$ for GCH against $64.7\%$ for TOA at a matching spatial precision, a 17.8 percentage-point lead that confirms that the explicit positional prior provides additional robustness when the input visual statistics shift away from the training distribution. The cross-position split additionally confirms that spatial selectivity is not a memorised centre bias. We deploy the full pipeline on the VOXL2 at 22–25 FPS within the DRIVOLUTION project. Full article

The transition to low-carbon energy systems requires large-scale expansion and spatial reconfiguration of electricity infrastructure. While power system planning models provide detailed techno-economic pathways for achieving decarbonization targets, their real-world implementation is frequently constrained by social acceptance. This study identifies a structural “Modelling Gap”—defined as the systematic divergence between how social factors are represented in optimization frameworks and how they manifest as institutional constraints in realized infrastructure deployment. Based on a systematic review of 76 research articles—comprising 43 modelling studies, 32 empirical studies, and 1 mixed contribution—this paper develops a five-pillar taxonomy to analyze how qualitative social variables are translated into formal decision-making constraints. The analysis reveals a fundamental divergence between modelling and empirical approaches. In optimization models, social acceptance is typically represented as a parametric variable—such as cost penalties, spatial exclusions, or weighted preferences—implying that social resistance can be mitigated through marginal adjustments. In contrast, empirical evidence shows that social friction often operates through institutional mechanisms, including permitting decisions, legal rulings, and administrative processes, which function as categorical constraints on infrastructure deployment. The results further demonstrate that current models systematically underrepresent key dimensions of implementation risk. In particular, temporal delays, regulatory dynamics, and project abandonment are only partially captured in existing frameworks, despite being major drivers of real-world outcomes. This mismatch leads to planning outputs that may be technically optimal but operationally infeasible. By identifying the structural limitations of current modelling approaches, this study contributes a conceptual foundation for integrating social acceptance into sustainable power system planning. The findings suggest that improving the alignment between optimization models and institutional realities is critical for developing sustainable energy system pathways that are not only cost-efficient, but also socially and legally implementable. Full article

Accurate modeling of deformable object dynamics is critical for robotic manipulation but remains challenging due to complex physics and strict physical constraints. In this paper, PhysCon-Deform is introduced, which is a mixed framework for specific tasks, combining residual neurodynamics learning and a differential augmented Lagrangian projection layer. Using a grid-based graph representation, PhysCon-Deform integrates a physics-based neurodynamics operator (PINDO) and a differentiable constraint projection module to achieve the deployment of residual correction, grid-based neurodynamics and model predictive control (MPC). Based on standard simulation benchmarks (Cloth3D, SoftGym and SoftMAC), our framework is always superior to the existing baselines in clean and disturbed environments. Specifically, it reduces long-term constraint violations by over 50%, demonstrates high robustness to end-effector trajectory noise, and enables efficient real-time trajectory optimization within an MPC pipeline. Extensive ablation and bias studies reveal that removing PINDO increases the prediction mean squared error (MSE) from 0.28 cm² to 0.68 cm² (a 143% increase), while omitting the constraint projection layer leads to a fourfold increase in violation rates. Furthermore, the robustness analysis of a colored noise and random walk drift model verifies its elasticity to non-ideal sensing. Although the deformation mechanism with a moderate rate-dependent effect in the simulation environment is optimized at present, PhysCon-Deform provides a very practical method to balance the precision-constraint trade-off in the control of deformable objects with physical constraints. Full article

Lunar navigation systems are being developed to support sustained surface operations, but their early deployment phase may involve limited satellite visibility and degraded accuracy in orbit and clock determination compared with terrestrial global navigation satellite systems (GNSS). Relative positioning with a lunar-surface reference station can mitigate common error sources through differenced measurements. In conventional GNSS, this mitigation often relies on the assumption that the line-of-sight (LOS) vectors from the reference and user receivers to the same satellite are nearly identical. Because lunar navigation satellite ranges can be considerably shorter than GNSS ranges, this approximation may be insufficient for kilometer-level surface baselines. This paper analytically quantifies three error mechanisms resulting from non-identical LOS geometries: the deterministic bias of the identical-LOS-vector approximation, the residual projection of broadcast ephemeris errors into single-differenced measurements, and the satellite-position-evaluation-time error caused by receiver clock initialization. For each mechanism, a simple closed-form bound is derived as a function of the baseline length and satellite range and is verified against time-series evaluations for representative lunar orbits. For a 10 km baseline in a low lunar orbit, the approximation alone can bias positioning by several tens of meters, whereas single differencing suppresses broadcast ephemeris errors to the meter level, and the clock-initialization effect can reach the meter level or larger. These results indicate that in lunar-surface relative positioning, the single-differenced range should be evaluated from the exact receiver-dependent geometry rather than from a common LOS vector. Additionally, the derived bounds provide a practical basis for budgeting broadcast ephemeris and receiver clock synchronization requirements. Full article

This study encourages mainstream schools to adopt inclusion processes aligned with their resources and capacity to foster own inclusive culture. Within a pilot study initially framed as a quasi-experimental design, the project dynamics demonstrated that participatory action research (PAR) enhances the experimental case study through stakeholder reflection. By implementing a Quality Deployment methodology centered on beneficiary needs, innovative solutions emerged for both students at risk of academic failure and those with high abilities, driven by the necessity to maximize their cognitive potential. Full article

Road traffic accidents continue to be a major global cause of fatalities, disproportionately affecting pedestrians and other vulnerable road users. While deep reinforcement learning has proven effective in handling complex navigation tasks, providing formal stability and safety guarantees during both training and deployment remains a significant challenge. This paper introduces a dual-layer safety-aware framework for pedestrian avoidance in occluded urban driving. During training, a first-order Control Lyapunov–Barrier Function is integrated with Proximal Policy Optimization to promote goal-reaching stability and obstacle avoidance: the analytic Lie derivatives of the Lyapunov and barrier functions are embedded as a modifier in the advantage estimate, providing explicit stability and safety signals that accelerate convergence toward safe, goal-reaching behavior without disrupting the standard policy update. At deployment, a higher-order Control Lyapunov–Barrier Function, realized through a quadratic programming safety filter, acts as a safety shield that projects the nominal acceleration onto the intersection of the second-order Lyapunov and barrier feasibility sets; the barrier function is further extended with relative velocity terms to account for dynamic pedestrian motion. Experiments with a four-wheeled vehicle in the Webots simulator show that the framework reliably reaches the goal, avoids an occluded pedestrian across a range of crossing speeds, and improves task success rates and safety-constraint adherence relative to Proximal Policy Optimization and a conventional higher-order safety filter baseline, particularly during emergency braking maneuvers. Full article

Integratingartificial intelligence (AI), the Internet of Things (IoT), and Building Information Modeling (BIM) holds considerable promise for modernizing construction management, yet a unified real-time framework connecting these technologies for heavy civil earthmoving remains lacking. This paper presents BIM-iDT, a BIM-Integrated Digital Twin framework that couples multi-source IoT sensing with an IFC-based BIM model to enable intelligent fleet coordination and automated progress control. The research follows a design-science methodology comprising framework formulation, modular development, field deployment, and multi-project validation. The framework comprises a heterogeneous sensor fusion layer aligning GPS, IMU, fuel-consumption, and LiDAR data within the BIM coordinate system; a spatio-temporal graph attention network (ST-GAT) that recognizes equipment states and predicts short-horizon productivity by modeling fleet-level spatial dependencies; a temporal point cloud differencing module that quantifies cut/fill volumes against BIM design surfaces; and a constrained multi-objective evolutionary optimizer (CMOEO) that generates Pareto-optimal dispatch plans balancing fuel, cycle time, utilization, and schedule adherence. Validation on a highway project with instrumented machines shows that ST-GAT achieves a macro-averaged F1 of 0.943, volume MAPE stays below 3%, and CMOEO reduces fuel consumption by 12.6% and cycle time by 9.3% while maintaining schedule adherence above 96%, yielding an estimated 168-ton CO₂ emission reduction. End-to-end latency averages 600 ms, satisfying real-time requirements. Cross-project transfer experiments on a secondary dam construction site further confirm framework generalizability, establishing BIM-iDT as a scalable paradigm for AI-and-IoT-enabled smart construction in infrastructure engineering. Full article

Teleoperated dual-arm underwater manipulators are a promising alternative to divers for hazardous subsea tasks, such as removing fishing nets from ship propellers. However, kinematic discrepancies between the master and slave can cause self-collisions between links that the operator cannot perceive, risking mechanical damage and costly underwater recovery. Real-time self-collision detection is therefore essential, yet existing approaches face an accuracy–efficiency trade-off: mesh-based methods such as GJK and FCL are accurate but computationally costly, while sphere and capsule approximations overestimate distances near joint interfaces, causing false positives. This paper proposes the Generatrix Distance Method (GDM), an analytical algorithm with bounded $\mathcal{O}\left(1\right)$ per-pair complexity for real-time self-collision detection. GDM approximates each link as a finite-length cylinder and classifies the configuration between two cylinders into four cases: Side–Side, Side–Cap, Cap–Side, and Cap–Cap. The Side–Side case admits a closed-form solution; cap-involved cases use a bounded-iteration cap-edge projection. Cylinder parameters are systematically derived from URDF kinematic information, enabling platform-independent deployment. GDM was validated through Gazebo simulations of a dual-arm underwater walking robot and teleoperation experiments on the ROBOTIS FFW-SG2 AI Worker. GDM runs about $10\times$ faster than FCL-Cylinder, while its iterative variant attains a 0.15 mm mean error at a $3\times$ speedup, confirming real-time suitability. Full article