Search Results (66)

Mobile laser scanning (MLS) enables rapid, high-resolution measurement of forest structure, yet its reliability for estimating stem volume and aboveground biomass (AGB) in dense plantations and its sensitivity to tree height uncertainty remain insufficiently quantified. This study evaluates handheld MLS for tree-level stem volume and AGB estimation in a mature Sitka spruce (Picea sitchensis (Bong.) Carr.) plantation in Ireland, using destructive sampling (n = 12) as a reference. MLS-derived diameter measurements were used to reconstruct stem profiles, with merchantable volume calculated by frustum integration to a 7 cm top-end diameter. The central objective was to quantify how uncertainty in tree height propagates through MLS-derived stem reconstruction and affects volume and AGB estimates. On average, 68.2% of merchantable stem volume was directly measured before upper-stem reconstruction. Under ideal validation conditions using true felled-stem height, MLS-derived merchantable volume and total AGB were estimated with RMSE values of 5.6% and 10.9%, respectively. Across practical height-input scenarios, error increased moderately, indicating that direct measurement of the lower stem constrained the propagation of height uncertainty. Compared with the nationally applied spruce allometric benchmark, the MLS-based workflow showed lower sensitivity to height-input uncertainty under the conditions evaluated. These findings demonstrate the potential of handheld MLS as a tree-level validation and calibration tool for measurement-based biomass assessment while highlighting the need for broader testing across stand types, species and operational plot-level workflows. Full article

Urban rooftop photovoltaic (RPV) systems are crucial for energy transition in the built environment. Although artificial intelligence (AI) has been widely adopted in this domain, existing studies remain methodologically fragmented and lack a workflow-oriented comparative synthesis. This study conducts a scoping review to systematically examine the methodological development and workflow evolution of AI-based urban RPV potential assessment. A total of 524 articles were initially retrieved from Web of Science and Scopus. In total, 48 peer-reviewed studies were selected through a structured screening process. The results reveal a clear transition from conventional machine learning toward deep learning, multimodal learning, and increasingly integrated hybrid workflows. Geometry-based, parameter-based, end-to-end estimation, and hybrid workflows were identified as the dominant workflow paradigms, reflecting different balances between automation, scalability, interpretability, and physical realism. The review further highlights challenges related to transferability, benchmarking heterogeneity, uncertainty propagation, and data dependency under heterogeneous urban conditions. Overall, this study provides a workflow-oriented synthesis and comparative analytical framework of AI-based urban RPV potential assessment through a workflow taxonomy perspective highlights future directions toward more generalizable, physically informed, and adaptive urban energy modelling frameworks for solar-integrated urban planning and built-environment decarbonization, and intelligent urban energy system development across heterogeneous urban contexts. Full article

Reliable surveillance of dissolved ²²²Rn in arid-region aquifers is challenged by very low natural activity and method-dependent biases, especially humidity sensitivity in electrostatic detectors and air–water partitioning during closed-loop aeration, which can obscure true concentrations needed for defensible drinking-water baselines under preventive frameworks. This study aimed to optimize and field-validate a low-background RAD7 Big-Bottle (RAD H₂O) closed-loop protocol tailored for arid conditions and apply it in a regional survey of groundwater used for potable supply in northeastern Saudi Arabia. Groundwater from wells across the region (shallow and deep completions) was collected and analyzed using isotope-resolved alpha spectroscopy (Po-218 and Po-214 windows) with strict chamber humidity control (≤7% RH), background checks, systematic blanks, duplicates, drift control (±10%), and uncertainty propagation. Air-phase chamber counts were mandatorily converted to water-phase activity using the CAPTURE parameterized by measured loop volumes, temperature, salinity, and humidity, and agreement was evaluated using regression diagnostics and Bland–Altman analysis. The optimized method achieved sub-Bq·L⁻¹ performance, with MDL improving from ~0.1645 Bq·L⁻¹ (30 min) to ~0.0233 Bq·L⁻¹ (1500 min) and ~0.0165 Bq·L⁻¹ (3000 min), and LOQ decreasing from ~0.50 to ~0.0707 and ~0.050 Bq·L⁻¹, respectively. Raw air-phase readings systematically overestimated dissolved radon by ~26% (slope ≈ 1.26), a bias removed by the validated air → water conversion. Surveyed ²²²Rn concentrations were uniformly low (0.03–3.20 Bq·L⁻¹), far below commonly used reference values (e.g., ~11.1 and ~100 Bq·L⁻¹), with no persistent spatial hotspots and broadly overlapping shallow/deep distributions, indicating variability dominated by local lithology and fracture-controlled flow rather than depth. A tiered monitoring scheme is recommended: short screening, routine baselining at ~900–1500 min total counting, and ~3000 min for ultralow verification, providing a transferable template for sustainable baseline programs in arid aquifers. Full article

Land surface temperature (LST) is a core physical parameter that characterizes land surface processes and surface-atmosphere energy exchange. As the demand for high-accuracy LST products intensifies across diverse research domains—including climate science, hydrology, and ecosystem modeling—the systematic quantification of pixel-level retrieval uncertainties has become essential for generating long-term, consistent Climate Data Records (CDRs). However, existing studies predominantly emphasize algorithmic development or localized validation, with limited attention to systematic cross-site and long-term uncertainty assessments. This gap impedes a comprehensive understanding of the compositional structure and spatiotemporal variability of LST retrieval uncertainties under heterogeneous surface and atmospheric conditions. In this study, based on the improved generalized split-window (GSW) algorithm and error propagation theory, the total uncertainty (U_total) and its four primary components—algorithm uncertainty (U_a), land surface emissivity uncertainty (U_e), noise equivalent delta temperature uncertainty (U_n), and atmospheric water vapor uncertainty (U_w)—at the pixel level over long time series and across multiple sites are quantified. Our analysis spans a 21-year period (2003–2023) and encompasses multiple geographically distributed sites, utilizing high-quality Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared data—specifically MYD11_L2 and MOD11_L2 products—collocated at the locations of 15 globally distributed ground-based reference sites. These sites are used to represent diverse climatic regimes and land-cover conditions, rather than to provide point-scale “true” LST values for residual-based validation. Results show that the interquartile range (IQR) of U_total is consistently concentrated between 1.0 and 1.2 K, demonstrating long-term stability. Systematic differences in U_total are identified across sensor platforms and diurnal cycles: U_total for Aqua/MYD data (1.13–1.25 K) is marginally higher than that for Terra/MOD data (1.05–1.17 K); similarly, daytime U_total (1.08–1.23 K) is generally slightly elevated relative to nighttime U_total (1.05–1.18 K). The contributions of individual uncertainty components to U_total exhibit substantial variation, with mean relative contributions of 81.97%, 11.32%, 4.46%, and 2.25% for U_e, U_a, U_n, and U_w, respectively. The dominant drivers of U_total differ markedly across climatic regions: in arid regions, U_total is predominantly governed by U_e, termed “emissivity-dominated,” accounting for over 85% of the total; conversely, humid tropical regions exhibit a “surface-atmosphere co-influenced” regime, characterized by a reduced contribution from U_e and correspondingly enhanced contributions from U_a and U_w. Furthermore, U_total decreases with increasing total column water vapor (TCWV) (Pearson correlation coefficient r = −0.498; linear slope k = −0.0425 K/(g/cm²)), and increases with increasing viewing zenith angle (VZA) (r = 0.208; k = 0.0022 K/degree). While U_a, U_n, and U_w all increase with TCWV, U_e decreases. Full article

Large-scale wind integration introduces significant harmonic degradation and resonance risks. Traditional strategies primarily targeting Total Harmonic Distortion (THD) often struggle with individual node violations and high investment costs. To address these challenges, this paper proposes a two-stage sequential coordination strategy for Passive Power Filters (PPFs) and Active Power Filters (APFs). First, stochastic harmonic emission and frequency-domain power flow models are developed to characterize wind-induced harmonic propagation. Second, a sequential optimization framework is established to minimize Life Cycle Cost (LCC). In the first stage, PPF siting and sizing are optimized for cost-effective, system-wide mitigation of low-order harmonics while ensuring THD compliance. The second stage utilizes targeted APF deployment to precisely suppress residual high-order violations and localized resonance. Chance-constrained programming is incorporated to manage wind power uncertainty, enhancing the scheme’s robustness. Simulations on an IEEE 17-bus system demonstrate that the proposed method effectively balances harmonic suppression performance with economic efficiency, providing a robust and cost-effective solution for wind farm power quality management. Full article

Unmanned aerial vehicles (UAVs) serve as both communication relays and aerial couriers in modern urban logistics networks. Conventional trajectory optimization methods assume perfect localization and isotropic free-space tracking signal propagation, which limits their effectiveness in urban canyons. To address the positional uncertainty and signal blockage from buildings, we propose a digital twin-driven framework for continuous trajectory and resource optimization in UAV swarms. We model an urban environment containing random high-rise structures, applying a non-line-of-sight (NLoS) uncertainty to reflect realistic communication degradation. The digital twin (DT) architecture utilizes a dual-layer spatial representation that captures a dynamically decaying positional uncertainty radius of the recipient. We define a strict visual localization boundary that initiates deterministic target tracking with a state transition mechanism. To manage the complexity of swarm routing, we apply Density-Based Spatial Clustering of Applications with Noise (DBSCAN), assigning one UAV courier and one logistics transfer station to each cluster. The system executes a continuous re-optimization loop using an adaptive multi-objective Genetic Algorithm. This framework jointly minimizes cumulative outage probability and total flight time while enforcing a signal-to-noise ratio threshold and throughput constraints. This continuous adaptation mechanism mitigates NLoS blockage risks, supporting reliable communication and efficient delivery in Global Navigation Satellite System (GNSS)-degraded and obstacle-dense urban environments. Full article

Accurate thermal characterization of building insulation materials is essential for reliable energy performance assessment, regulatory compliance, and the development of high-performance envelopes. On one hand, the growing adoption of innovative insulating products, such as nanoporous materials, aerogel-based composites, bio-based panels, and thin insulating coatings, helps to enhance buildings’ energy efficiency by means of sustainable raw materials. On the other hand, conventional measurement techniques encounter significant challenges, due to their heterogeneity, reduced thickness, and unconventional geometries. In this study, an intra-laboratory comparison of three widely used methods for thermal conductivity determination is presented: the Transient Plane Source (TPS, Hot Disk) method, the Guarded Hot Plate (GHP) method, and the Heat Flow Meter (HFM) method. A total of twelve insulating materials, spanning super-insulating cores, insulating renders, bio-based panels, and nanocomposite coatings, were experimentally characterized under controlled laboratory conditions. A view on the analyzed insulating materials’ cradle-to-grave environmental impact is also given, to enhance the users’ awareness for the highly informed choice. The results highlight systematic differences between transient and steady-state approaches, with TPS measurements generally exhibiting larger deviations for materials characterized by surface roughness, limited thickness, or strong internal heterogeneity. In contrast, GHP and HFM methods show closer agreement when specimen geometry and stabilization requirements are satisfied. The influence of contact resistance, probing depth, specimen preparation, and uncertainty propagation is critically analyzed for each technique. The study provides practical insights into the applicability limits of commonly used thermal characterization methods and emphasizes the importance of selecting measurement techniques in relation to material morphology and testing constraints. These findings support more reliable thermal property assessment of emerging insulation materials and contribute to improved consistency between laboratory measurements and energy performance evaluations for buildings. Full article

Platelet-rich plasma (PRP) therapies are increasingly used in musculoskeletal and regenerative medicine; however, substantial variability in reported outcomes persists even when similar preparation protocols are employed. In quantitative terms, PRP preparation can be interpreted as a stochastic process in which uncertainty propagates through multiple biological and technical inputs. Herein we propose a probabilistic framework to quantify variability in the platelet dose delivered (PDD) using Monte Carlo simulations. The platelet dose was formulated as a random variable defined by a multiplicative model involving the platelet count (modeled as a normal distribution), concentration factor (log-normal), injected volume (uniform), and processing efficiency (beta). Input parameters were represented by probability distributions derived from ranges reported in the literature, and uncertainty propagation was explored through 100,000 Monte Carlo iterations. The resulting simulations revealed a wide dispersion in PDD, characterized by a right-skewed distribution with a median of 3.1 × 10⁹ platelets and an interquartile range of 1.9 × 10⁹ platelets, yielding a coefficient of variation exceeding 50%. Sensitivity analysis based on variance-based global sensitivity measures (Sobol indices) identified the injected volume and concentration factor as the dominant contributors to output variability, with substantial interaction effects between these parameters accounting for a considerable portion of total variance. The baseline platelet count and processing efficiency had comparatively smaller effects. These results demonstrate how probabilistic modeling can clarify the sources of variability in PRP preparation and provide a generalizable framework for uncertainty quantification in multiplicative biomedical systems. Full article

Multimodal freight transportation networks are increasingly exposed to natural and human-made disruptions, yet prior research remains fragmented in how disruptions are represented, which modeling techniques are applied, and how results are validated, limiting comparability and actionable guidance for resilient planning. This study presents a PRISMA-guided systematic review of disruption modeling in multimodal freight networks. A total of 21 studies were identified and coded to address three research questions concerning (RQ1) which analytical and computational modeling techniques are applied; (RQ2) to what extent models represent cross-modal interdependencies, cascading failures, and recovery processes; and (RQ3) what validation, calibration, and empirical testing strategies are employed. The review shows that optimization-based approaches and hybrid frameworks dominate the literature, complemented by fewer network science and data-driven methods. Most studies model disruptions as node/link failures and/or capacity degradation using static single-event scenarios, and explicit representations of cascading effects, operational delay propagation, and time-evolving recovery trajectories remain relatively rare. While many studies rely on real network data, formal calibration and historical backtesting against observed disruption events are uncommon, and validation is primarily case study-based. These findings highlight the need for more dynamic resilience modeling, stronger uncertainty quantification, standardized reporting of performance and resilience metrics, and greater use of empirically grounded validation to improve the generalizability and decision relevance of multimodal freight resilience models. Full article

Decarbonizing the maritime sector requires not only adopting alternative fuels and propulsion technologies but also quantitatively assessing their impacts on coastal and urban air quality. This study develops a stochastic, time-resolved air-quality modelling framework to evaluate ship-related pollutant dispersion in port environments. The approach integrates Automatic Identification System (AIS) trajectories, vessel-specific emission factors, and meteorological inputs within a moving-source Gaussian dispersion model to simulate the spatio-temporal evolution of pollutant concentrations. A 24 h case study for the Ports of Los Angeles and Long Beach demonstrates highly intermittent emission behaviour, with peak aggregated emission rates reaching approximately 1.2 kg/s for CO₂ and 3.8 g/s for SO₂. Temporally integrated concentration fields reveal maximum cumulative dosages of 0.145 g·s/m³ for NO_x, 0.023 g·s/m³ for SO₂, 0.014 g·s/m³ for total PM, and 7.5 g·s/m³ for CO₂ in near-port traffic corridors. Sensitivity analysis indicates that effective emission height variations alter cumulative exposure by up to 17%, whereas temporal resolution changes produce deviations below 7%, confirming numerical stability. Monte Carlo uncertainty propagation demonstrates bounded but non-negligible variability in exposure estimates under realistic emission and wind uncertainties. Results show that cumulative exposure patterns differ substantially from short-term concentration peaks, highlighting the importance of time-integrated and receptor-based metrics for port air quality assessment. The proposed AIS-driven stochastic framework provides a reproducible and computationally efficient tool for evaluating operational mitigation strategies and supporting evidence-based maritime decarbonization pathways. Full article

This study develops a hybrid deep learning framework for point and interval prediction of Total Nitrogen (TN) concentrations in the Pearl River Delta, China. To address the inherent stochasticity of water quality systems, Bidirectional Long Short-Term Memory (BiLSTM) networks are integrated with residual learning blocks (Res) and Bayesian Optimization (BO). The resulting BiLSTM-Res-BO framework is evaluated within a comparative analysis of eight forecasting models that combine BiLSTM and BiGRU architectures with two uncertainty quantification approaches: Quantile Regression (QR) and Monte Carlo Dropout (MCD). Results from 37 monitoring stations demonstrate that the effectiveness of residual learning is highly context-dependent. For point forecasting, BiLSTM-Res achieves substantial performance gains (12.5–15% RMSE reduction) at complexity-sensitive sites, while providing negligible or slightly degraded performance under hydrologically stable conditions. For interval forecasting, QR-based residual models—particularly Q-BiLSTM-Res—produce notably narrower prediction intervals, with interval width reductions of 16.7–27.3% relative to the baseline BiLSTM model, under comparable levels of empirical coverage. In contrast, MC-dropout-based methods tend to yield wider intervals with different coverage–width trade-offs, reflecting distinct uncertainty propagation behaviors across modeling frameworks. Full article

Despite existing protection limits established by different health agencies and regulatory bodies, chronic exposure to non-ionising electromagnetic field radiation (NIR) has raised concerns about its potential biological effects and its impact on human health. Exposure to NIR in urban environments is almost inevitable due to the density of devices and communication systems that emit these waves. Correctly measuring exposure levels among city residents is key to determining whether there is a relationship between these levels and potential health problems associated with NIR. Several factors, including the ubiquity of electromagnetic fields (EMFs) and people’s unawareness of their exposure, make the NIR assessment challenging. This paper proposes a standardised procedure for NIR testing and measurement for frequencies from 100 kHz to 3 GHz, designed explicitly for outdoor urban environments. The measurement procedure is intended for populated urban areas, a complex environment for signal propagation. The complete procedure, techniques, and equipment used for wideband and narrowband measurements are detailed, along with their corresponding overall uncertainty budgets. The data collected by this procedure are suitable and valuable for comparative epidemiological studies due to a systematic measurement protocol and rigorous control of measurement uncertainty. The proposed measurement procedure has been tested in two cities in central Spain, with a total population of 262,000. A total of 534 measurement points have been performed. The results can be used to verify compliance with exposure limits and to demonstrate levels below the applicable regulatory limits. Furthermore, it has been possible to test the validity of the hypothesis that urban environments can be characterised by NIR exposure, which was postulated in this work based on an ITU-R-inspired simplification that classifies urban outdoor areas into representative exposure categories. Full article

Leaf Area Index (LAI) is a key biophysical descriptor of crop canopies and is essential for growth monitoring and yield estimation. We present a physics-driven machine-learning framework for operational LAI retrieval and end-to-end uncertainty quantification that couples the PROSAIL radiative transfer model with a genetic-algorithm-optimised multilayer perceptron (NN–GA). PROSAIL is sampled across plausible parameter priors and spectra are convolved with Sentinel-2B spectral response functions to build a 30,000-sample training library; a GA is used to globally optimise network weights and biases. Total retrieval uncertainty is decomposed into a simulation component (PROSAIL parameter variability) and a training component (variability across repeated NN–GA trainings) and combined via the law of propagation of uncertainty. The model was developed in Minqin (modelling/testing area; entirely maize) and transferred to Zhangye (transfer/validation area; predominantly maize, with one sunflower plot). Sentinel-2B validation results were RMSE/R² = 0.44/0.73 (Minqin) and 0.40/0.56 (Zhangye), indicating reasonable cross-site generalisation. The uncertainty split indicates physical-driven contributions of 11.42% and 11.48% and machine-learning contributions of 18.06% and 12.96%, respectively. The framework improves 10 m LAI retrieval accuracy and supplies a reproducible, per-pixel uncertainty budget to guide product use and refinement. Full article

We establish a general, device-oriented procedure to extract absolute pump-band metrics from room-temperature UV–Vis (ultraviolet–visible) absorbance—including the absorption coefficient α(λ), per-active-ion cross-section ${\sigma }_{\mathrm{e}\mathrm{f}\mathrm{f}}\left(\lambda \right)$, the effective per-active-ion absorption cross section ${\sigma }_{\mathrm{e}\mathrm{f}\mathrm{f}}\left(\lambda \right)$ and derivative-based line-shape descriptors. As a representative case study, the procedure is applied to nanocrystalline Er³⁺/Ho³⁺:Y₃Ga₅O₁₂ over the 350–700 nm spectral range. After baseline correction and line-shape inspection assisted by the numerical second derivative of the absorbance, we extract conservative peak positions and the full width at half maximum across the visible 4f–4f manifolds. At the technologically relevant pump wavelengths near 406 nm (Er-addressing) and 473 nm (Ho-addressing) bands, resulting absorption coefficients are α = 0.313 ± 0.047 cm⁻¹ and α = 0.472 ± 0.071 cm⁻¹, respectively. The corresponding per-active-ion ${\sigma }_{\mathrm{e}\mathrm{f}\mathrm{f}}$ of (3.62 ± 0.54) × 10⁻²² cm² and (5.46 ± 0.82) × 10⁻²² cm², referenced to the measured optical path length L = 0.22 ± 0.03 mm (approximately 15% propagated relative uncertainty; explicit 1/L rescaling). Cross sections are reported per total active-ion density (Er³⁺ + Ho³⁺). The spectra exhibit Stark-type substructure only partially resolved at room temperature; the second derivative highlights hidden components, and we report quantitative descriptors (component count, mean spacing, curvature-weighted prominence, and pump detuning) that link line-shape structure to absolute pump response. These device-grade metrics enable rate-equation modelling (pump thresholds, detuning tolerance), optical design choices (path length, single/multi-pass or cavity coupling), and host-to-host benchmarking at 295 K. The procedure is general and applies to any rare-earth-doped material given an absorbance spectrum and path length. Full article

Under the global imperative for climate action and sustainable development, accelerating the transition towards high-penetration renewable energy systems remains a universal priority, central to achieving the United Nations Sustainable Development Goals. However, the inherent uncertainty and volatility of renewables such as wind and solar PV pose fundamental challenges to power system stability and flexibility worldwide. These challenges, if unaddressed, could significantly hinder the reliable and sustainable integration of clean energy on a global scale. While pumped storage hydropower (PSH) represents a mature, large-scale solution for enhancing system regulation capabilities, existing planning methodologies frequently suffer from critical limitations. These included oversimplified scenario representations—particularly the inadequate consideration of escalating extreme weather events under climate change—and computational inefficiencies in solving large-scale stochastic optimization models. These shortcomings ultimately constrained the practical value of such approaches for advancing sustainable energy planning and building climate-resilient power infrastructures globally. To address these issues, this paper proposed a bi-level stochastic planning method integrating scenario optimization and improved Benders decomposition. Specifically, an integrated framework combining affinity propagation clustering and isolation forest algorithms was developed to generate a comprehensive scenario set that covered both typical and anomalous operating days, thereby capturing a wider range of system uncertainties. A two-layer stochastic optimization model was established, aiming to minimize total investment and operational costs while ensuring system reliability and renewable integration. The upper layer determined PSH capacity, while the lower layer simulated multi-scenario system operations. To efficiently solve the model, the Benders decomposition algorithm was enhanced through the introduction of a heuristic feasible cut generation mechanism, which strengthened subproblem feasibility and accelerated convergence. Simulation results demonstrated that the proposed method achieved a 96.7% annual renewable energy integration rate and completely avoided load shedding events with minimal investment cost, verifying its effectiveness, economic efficiency, and enhanced adaptability to diverse operational scenarios. Full article