Search Results (76)

Developing stable plant-based probiotic beverages requires innovative formulation strategies. While chickpea aquafaba, a protein-containing by-product, shows promise for the development of functional foods, its specific application in probiotic matrices remains underexplored. This study evaluated a coconut-based probiotic beverage supplemented with aquafaba by monitoring viable cell counts, pH, sugar and organic acid profiles, and antioxidant activity during 30 days of refrigerated storage. A 2² full factorial experimental design was used to investigate the effects of aquafaba and sucrose concentrations on the viability of Lacticaseibacillus casei NRRL B-442. The selected formulation (30% aquafaba and 50 g/L sucrose) achieved the highest viable cell count after fermentation (9.88 ± 0.09 log CFU/mL), compared with 8.40 ± 0.07 log CFU/mL in the control (coconut). During 30 days of refrigerated storage, the formulation R4 maintained probiotic viability above the recommended functional threshold (>7.0 log CFU/mL), reaching 7.40 log CFU/mL at day 30, whereas the control (coconut) declined to 6.77 log CFU/mL. R4 formulation also exhibited a slower decline in pH (4.50 vs. 3.20 in the control (coconut) at day 30) and higher lactate concentration after fermentation (2.04 vs. 0.79 g/L). Carbohydrate profiling revealed sustained sucrose utilization during storage, while the control (coconut) showed early metabolic stabilization. Monte Carlo simulation estimated an 87.9% probability for R4 and 24.8% for the coconut control of meeting the predefined viability criterion of 7.0 log CFU/mL under the modeled storage conditions. R4 beverage maintained viable cell counts above the predefined criterion of 7.0 log CFU/mL throughout storage, unlike the control (coconut). These results emphasize that integrating formulation optimization with predictive microbiology and stochastic modeling is a key component for developing robust probiotic plant-based beverages. This approach provides a complementary framework for evaluating uncertainty in predicted probiotic viability under the evaluated storage conditions. Full article

Bio-inspiration has mainly been used to represent organisms, behaviors, or natural processes in the design of metaheuristics. This study proposes a different use: drawing on a biological phenomenon to model the temporal evolution of an internal parameter in an existing algorithm. Logarithmic Mean Optimization (LMO) is adopted as a case study, focusing on $\beta$, which scales the stochastic perturbation term and regulates the balance between exploration and exploitation. Inspired by the progressive transition observed during bird landing, a normalized arctangent trajectory controlled by m and k is proposed. Both hyperparameters were tuned through Bayesian optimization using the Tree-structured Parzen Estimator (TPE) implemented in Optuna. The 23 benchmark functions were divided into 12 tuning functions and 11 independent test functions. Nine $\beta$ configurations were evaluated through 31 runs per function. The Friedman test showed significant differences among the variants ($p=3.8269\times {10}^{-10}$), and the proposed formulation achieved the best average rank (1.7273). Post-hoc Wilcoxon tests with Holm correction found significant differences against two of the eight alternatives. Overall, the results suggest that bio-inspiration, when used as a criterion for designing adaptive parameter-control mechanisms, can yield improvements and be considered a potential alternative in the design of new metaheuristic algorithms. Full article

Carcinine is an imidazole dipeptide with potent antioxidant and antiglycation properties, although its chemical synthesis currently relies on severely environmentally harmful processes. In this work, a multi-enzyme cascade biotransformation system comprising 4′-phosphopantetheinyl transferase and non-ribosomal peptide synthetase was constructed. To overcome the limitations arising from stochastic spatial distribution and suboptimal mass transfer associated with independent enzymes, a fusion protein strategy was adopted. The two enzymes were fused via a flexible genetic linker within plasmid pET28a-SFP-L-Ebony, which enabled robust soluble expression in Escherichia coli. Concurrently, the endogenous peptidase genes (pepA, pepB, pepD, and pepN) were systematically knocked out using CRISPR/Cas9-mediated gene editing. This quadruple protease-deficient strain (designated SFP-L-Ebony-ΔpepABDN) effectively suppressed product degradation. Subsequent optimization revealed that optimal catalytic performance occurred at 25 °C and pH 7.0. The highest biotransformation efficiency was achieved using 15 g/L crude enzymes, in the presence of 2 mM ATP and 10 mM MgCl₂. Through a fed-batch substrate feeding strategy in a 50 mL reaction system, the final carcinine titer reached 7.0 g/L after 48 h. This study, therefore, provides an efficient and sustainable technological pathway for the green biomanufacturing of carcinine as well as other high-value dipeptides. Full article

This paper investigates the precision and stochastic error propagation of navigation solutions obtained from a low-cost microelectromechanical system inertial measurement unit (MEMS-IMU) under static conditions. A modern smartphone equipped with an MEMS-IMU was rigidly mounted at a calibrated fixed location to establish a zero-reference scenario, and inertial measurements were collected while the device remained stationary. The dataset was divided into 75 non-overlapping segments, each comprising 30 s of data sampled at 10 Hz, to enable statistically robust analysis. For each segment, velocity and position, which are theoretically zero under static conditions, were computed using strapdown inertial mechanization. A comprehensive statistical framework was then applied to characterize the stochastic behavior of both the raw inertial measurements and the derived navigation states. The methodology first assessed data normality, stationarity using the Augmented Dickey–Fuller (ADF) test, and variance homogeneity using Bartlett’s test. Subsequently, ARIMA models were identified and validated using the Ljung–Box (LB) test, while power spectral density (PSD) analysis provided complementary frequency-domain characterization. In addition, a multivariate, non-negative least squares variance component estimation (NNLS-VCE) method was employed to jointly estimate the variance components of multiple navigation state variables. The results demonstrate that the accelerometer and gyroscope measurements along all three axes are well characterized as stationary white-noise processes, with standard deviations in the order of ${10}^{-2} \mathrm{m}/{\mathrm{s}}^2$ and ${10}^{-4} \mathrm{rad}/\mathrm{s}$, respectively. The estimated velocity random walk (VRW) coefficients are $\left[0.197,0.201,0.160\right] \mathrm{m}/\mathrm{s}/\sqrt{\mathrm{h}}$, while the corresponding angular random walk (ARW) coefficients are $\left[0.009,0.012,0.008\right] \mathrm{rad}/\sqrt{\mathrm{h}}$. In contrast, the derived velocity and position estimates exhibit random walk behavior caused by error accumulation in the inertial mechanization process and are best represented by ARIMA$(0,1,0)$ and ARIMA$(0,2,0)$ models, respectively, consistent with the corresponding Allan variance analysis. After 30 s of static navigation, the average standard deviations of the ENU velocity estimates are ${\sigma }_v=[0.77,0.44,0.29] \mathrm{m}/\mathrm{s}$, while the corresponding position standard deviations are ${\sigma }_p=[1.30,0.69,0.46] \mathrm{m}$. The proposed framework provides a comprehensive approach for the stochastic modeling, precision assessment, and error characterization of low-cost MEMS-IMU navigation systems. Full article

Driven by the low-carbon transformation in the construction industry, Moso bamboo has emerged as a promising green material. However, a critical gap exists in current structural design theories: they predominantly rely on homogeneous assumptions, failing to capture the inherent spatial variability and coupled strength–stiffness degradation of bamboo. Experimental results reveal a distinct longitudinal gradient, where the top section (5–8 m) exhibits approximately 15% higher average tensile strength and 12% higher average elastic modulus compared to the bottom section (1–3 m). This oversight severely compromises the accuracy of structural reliability evaluations. To address this, this study pioneers a high-precision digital representation method by developing a novel three-dimensional (3D) anisotropic bivariate coupled random field model. Experimental and stochastic finite element simulations demonstrate that the model accurately replicates spatial variations, maintaining relative errors for primary statistical indicators strictly below 1% while robustly capturing the bivariate coupling characteristics. Crucially, by integrating non-parametric probability box (P-box) theory, the macroscopic tensile resistance of full-scale bamboo members is rigorously bounded within a definitive statistical interval. Furthermore, the extracted Interval Skewness Ratio generally remains greater than 1.0 (averaging 1.38), providing robust quantitative proof of an asymmetric structural degradation governed by the brittle weakest-link failure mechanism. By effectively eliminating non-physical sample generation associated with traditional univariate models, this research makes a significant contribution to the field, providing a rigorous digital twin framework and theoretical foundation for the advanced non-probabilistic safety design of modern bamboo structures. Full article

Smartphone global navigation satellite system (GNSS) positioning is degraded by low-cost antennas, limited receiver hardware, multipath propagation, and noisy code pseudorange observations. Existing correction methods often improve stochastic weighting, estimate coordinate-domain corrections, or smooth receiver trajectories, but they rarely estimate how each satellite contributes to the horizontal position error while preserving line-of-sight (LOS) geometry. This study presents a random-forest-assisted geometry-aware correction method that combines satellite-wise LOS projection error estimation with exponential temporal weighted least squares (Temporal WLS). The horizontal error between the smartphone National Marine Electronics Association (NMEA) solution and the F9P reference position is projected onto each satellite LOS direction and used as the learning target. A random forest model is trained using 26 smartphone GNSS features, including geometry, signal strength, code-derived variation, uncertainty, automatic gain control, and state flags. The predicted LOS errors are fused with satellite geometry through epoch-wise WLS and Temporal WLS. In same-session front-70/back-30 validation, the horizontal root mean square (RMS) error decreased from 2.747 m to 1.033 m. Excluding one suspected non-co-located reference session further reduced the RMS error from 2.867 m to 0.362 m. Full article

This work aims to study and improve the long-term stability of conductimetric biosensors for urea detection in clinical and environmental samples, which are fundamentally limited by complex thermal and temporal drifts due to temperature-sensitive enzyme kinetics, variations in ionic mobility, and the progressive degradation of the sensing layer. The biosensor targets the urea concentration range 0.01–30 mM, validated against experimental data and covering the clinically relevant range for blood urea detection (2.5–7.5 mM), urine (20–40 mM), and environmental monitoring applications. Conventional calibration techniques, such as the conventional calibration method (based on reference measurements), and purely deterministic correction methods, such as deterministic methods (based on known fixed equations), often prove insufficient because they struggle to capture the non-stationary and inherently stochastic nature of these drifts. In this work, we propose an original hybrid probabilistic framework that synergistically combines machine learning and Bayesian inference for robust adaptive drift compensation. A Random Forest model is first implemented to model the deterministic nonlinear relationships between environmental parameters (temperature, pH, CO₂ concentration) and the sensor response. The residual temporal drift is then explicitly modeled as a non-stationary latent stochastic process using Bayesian inference based on a Gaussian process. This approach allows continuous online model updating, real-time uncertainty quantification, and automatic detection of anomalies. The models were trained and validated on a large dataset obtained from multiphysics simulations carried out in COMSOL Multiphysics 5.6. These simulations incorporated enzymatic reactions, thermal effects, and chemical dynamics taking place inside the sensor. Experimental results show that the hybrid approach substantially enhances sensor performance, lowering the root mean square error (RMSE) to below 0.8 μS/cm (corresponding to less than 0.5% of the full-scale response) over a wide temperature range (15–45 °C) and across extended operating periods. This represents a clear improvement over conventional compensation method. By merging the predictive power of ensemble learning with a probabilistic Bayesian model of dynamic drift, this study introduces a fresh perspective on the design of intelligent, self-adaptive, and drift-resistant conductimetric biosensors. The proposed framework holds strong potential for reliable, long-term autonomous operation in urea reliable, long-term autonomous operation in urea monitoring across biomedical diagnostics (kidney/liver function assessment) and environmental surveillance (water eutrophication prevention). Full article

The Santiago River is a highly anthropogenically impaired lotic system globally, yet the mechanisms governing its contaminant transport remain poorly understood under static monitoring paradigms. This study evaluates how hydrological forcing dictates the mobilization and bioavailability of trace metals by integrating a 15-year public hydrochemical database from 10 monitoring nodes with SAR-derived discharge estimates and thermodynamic metal modeling (PHREEQC). To validate the structural integrity of the mass load estimates against hydrometric uncertainties, a deterministic boundary-sensitivity analysis was conducted. Results empirically refute the classical dilution paradigm, introducing the “Anthropogenic Pulse” to describe the non-linear acceleration of pollutant export during high-flow events (discharge Q surging from 36.62 to 286.13 m³/s). While climate-driven parameters follow seasonal cycles, industrial stressors (COD, Pb, Cd) remain in a chronic steady state, decoupling from volumetric dilution. Based on coupled × CQ × C (discharge × concentration) estimates, this dynamic induces a synchronized flushing of toxic burdens, exporting monthly peak loads exceeding 51,000 kg of Zinc, 6500 kg of Lead, and 3100 kg of Cadmium. Thermodynamic simulations reveal that this hydrological flushing functions as a chemical activator; the seasonal dilution of natural Alkalinity and Hardness suppresses the river’s theoretical buffered pH (from 8.5 to 7.0), maintaining metals in their uncomplexed free-ion states (Me²⁺). Modeling indicates that nearly 90% of the exported Cadmium remains in this highly labile, toxic form due to a dual coupling with both river Discharge (r_s = 0.87) and pH (r_s = 0.79). The identification of stochastic arsenic peaks 100 times above regulatory limits at Paso de Guadalupe (RS-08) underscores the failure of concentration-based monitoring. Our findings suggest that restoration strategies should shift toward mass-loading-based regulatory frameworks and targeted sediment management at critical nodes to mitigate the chronic export of bioavailable industrial waste. Full article

Accurate Unmanned Aerial Vehicle (UAV) position estimation is the cornerstone of urban low-altitude safety management systems. Time Difference of Arrival (TDOA) and Remote Identification (Remote ID) are widely used surveillance technologies with complementary characteristics. TDOA provides high-rate updates but suffers from geometry-induced horizontal–vertical anisotropy and multipath effects, while Remote ID supplies absolute state information yet struggles with intermittent sampling and packet loss. Existing fusion schemes typically address these issues in isolation: sequential filtering manages asynchrony but assumes Gaussian noise, robust estimators suppress outliers at the cost of discarding valid data, and coupled-filter architectures allow vertical anomalies to contaminate horizontal estimates through the Kalman gain cross-coupling. No prior framework jointly handles structural TDOA altitude jumps, stochastic Remote ID timing jitter, and the geometric anisotropy between estimation subspaces within a single coherent pipeline. To bridge this gap, we propose a Hybrid Conditional Kalman Filter (HCKF) framework comprising three integrated modules. First, a kinematics-based temporal alignment module maps asynchronous measurements onto a uniform timeline and predicts missing samples, resolving cross-modal time mismatches. Second, a measurement quality evaluation mechanism detects TDOA altitude steps via robust two-layer stratification and scores Remote ID timing irregularity through a confidence mapping, converting these anomalies into dynamic covariance adjustments and weight caps without discarding observations. Third, a Subspace-Decoupled Fusion strategy exploits the physical insight that TDOA horizontal precision derives from hyperbolic intersection geometry, whereas its vertical estimates suffer from weak observability due to near-coplanar ground-station deployment. By applying entropy-guided weighting in the horizontal plane and a conditional Remote ID-dominant rule in the vertical axis, this design prevents cross-dimensional error propagation. The framework was validated using three real-world flight missions at distinct altitudes (255 m, 345 m, and 440 m) totaling 13.51 km of flight distance, with RTK serving as ground truth. HCKF reduces the Root Mean Square Error by over 40% relative to single-source baselines (95% bootstrap confidence interval: [35.2%, 48.7%]), and paired Wilcoxon signed-rank tests confirm statistically significant improvement ($p<0.01$) over standard EKF, Covariance Intersection, and Iterative CI across all three tracks. Full article

Soil depth and habitat degradation can reshape fungal communities in salt-affected wetlands, but their effects on fungal ecological processes remain insufficiently understood. This study examined soil fungi in the Halahai Provincial Nature Reserve and adjacent converted farmland in the western Songnen Plain, Northeast China, where salt-affected meadow soils correspond mainly to Solonetz. Four habitat types—reed wetland, meadow steppe, degraded Suaeda saline patch, and converted farmland—were sampled at 0–20 cm and 20–40 cm soil depths. Soil properties, fungal diversity, taxonomic composition, environmental associations, niche breadth, assembly processes, and FUNGuild-based trophic modes were analyzed using ITS sequencing. Degraded Suaeda soils showed the strongest salinity–alkalinity stress, with pH values of 10.34–10.30 and electrical conductivity of 1.70–1.75 dS·m⁻¹. Fungal richness was highest in surface-converted farmland, with a Sobs value of 423.33, and lowest in deeper degraded Suaeda soil, with a Sobs value of 86.00. Ascomycota dominated most groups, especially degraded Suaeda soils, where its relative abundance reached 75.29–76.80%. ANOSIM confirmed significant community dissimilarity among habitat-depth groups (R = 0.56878, p = 0.001). Specialists accounted for 68.07% of fungal taxa, and stochastic processes, especially drift and dispersal limitation, contributed substantially to assembly. These results indicate that soil depth, salinity–alkalinity, and habitat conversion jointly regulate fungal community structure and ecological processes in degraded soda saline–alkali wetlands. Full article

The proliferation of AI-generated content and personalized AI systems has sharpened two fundamental and related computational problems: the progressive erosion of authentic identity in AI-mediated representations, and the growing difficulty of distinguishing human-originated from AI-generated behavioral and textual streams. This paper proposes a rigorous computational framework in which digital identity is formalized as a holomorphic function of complex time T = (a + ib) ∈ ℂ, where the real component Re$\left(T\right)$ encodes chronological progression and the imaginary component Im$\left(T\right)$ spans a continuum from episodic memory ($Im\left(T\right) < 0$) through the present moment ($Im\left(T\right) = 0$) to prospective imagination ($Im\left(T\right) > 0$). We argue that holomorphicity—enforced via Cauchy–Riemann regularization during CTNN learning (Proposition 1)—provides a theoretically grounded encoding of identity coherence, and discuss its advantages over alternative mathematical choices, including Lipschitz continuity, ${\mathit{C}}^{\mathbf{\infty }}$ smoothness, piecewise analytic functions, and stochastic models. Under four explicit Assumptions 1–4 covering the Markovian structure and fixed context window of current LLM architectures, we establish via Lemmas 1 and 2 and Theorem 1 that AI-generated behavioral trajectories exhibit structural limitations in satisfying the Cauchy–Riemann conditions at temporal depths characteristic of human biographical memory—limitations that do not arise for human trajectories learned under CTNN regularization. Building on this result, we introduce the Human–AI Authenticity Discriminant (HAAD), a theoretically grounded classifier with a fully specified calibration algorithm and sensitivity analysis ($\mathit{\kappa }$ ΔAUROC ≤ 0.04 over ±30% perturbation). Five metrics—TCS, ISI, PAS, GAS, and HAAD—are derived analytically from the holomorphic structure. The algorithmic framework is instantiated on four real-world datasets: MovieLens 25M, the Pushshift Reddit corpus, the Stack Overflow Data Dump, and the LIAR dataset. On the LIAR benchmark, TDT-HAAD achieves AUROC = 0.82 (95% CI: [0.79, 0.85]), exceeding a RoBERTa-based LLM detector baseline (AUROC = 0.75, DeLong p < 0.01); an ablation study supports the structural contribution of each component. A credibility harvesting signature is detectable 45.3 ± 12.1 days before standard temporal models reach statistical significance. Full article

Objective: Rapid vertical manoeuvres and intermittent vibration in autonomous electric vertical take-off and landing (eVTOL) aircraft can provoke pronounced psychological anxiety in passengers. To address this, we propose a closed-loop adaptive system that integrates an in-ear wearable sensor with dynamic regulation of the cabin microenvironment, enabling real-time monitoring of each passenger’s autonomic state and delivering individualised mitigation through a continuous sense–analyse–intervene–feedback loop. Methods: The system is built around a pair of custom in-ear modules that integrate dual-wavelength photoplethysmography (PPG; 525 nm green and 940 nm infrared), galvanic skin response (GSR), and a six-axis inertial measurement unit (IMU) sampled at 200 Hz. To suppress the 20–80 Hz vibration generated by the distributed electric propulsion system, a compliant silicone damping sleeve attenuates high-frequency components at the hardware level, while a Kalman filter fuses the IMU and PPG streams and an adaptive notch filter removes residual rotor harmonics. The pipeline raises the heart-rate-variability (HRV) signal-to-noise ratio (SNR) to 24.1 dB, with a Pearson correlation of 0.96 against a medical-grade chest strap. A hybrid CNN–LSTM network—two convolutional layers (32 filters each) followed by two LSTM layers (128 hidden units)—predicts impending anxiety from HRV time-domain features (RMSSD, pNN50) and frequency-domain features (LF/HF ratio), triggering intervention 8.2 s in advance on average. According to the predicted anxiety level (mild/moderate/severe), a fuzzy controller modulates transcutaneous auricular vagus nerve stimulation (1–5 mA), the binaural-beat frequency (4–8 Hz, theta band), and the cabin lighting colour temperature (2700–6500 K) in real time. The intervention parameters are continuously refined by SPSA-based stochastic optimisation of the HRV recovery rate (step size 0.01; updated every 30 s). Results: In a randomised controlled experiment conducted in a simulated flight environment (N = 50; aged 22–45 years; 1:1 sex ratio), the active group reached physiological recovery in 52.3 s on average, compared with 98.6 s for the sham-controlled group—a 47% reduction (Cohen’s d = 1.24, p < 0.001). User acceptance reached 94%. Conclusions: The proposed in-ear platform enables closed-loop adaptive regulation of anxiety in the eVTOL cabin and overcomes the limitations of conventional passive mitigation strategies. By combining vibration-tolerant physiological sensing with multimodal environmental control, the work offers a practical pathway for improving passenger experience in urban air mobility and provides a useful reference for human-factors standards governing autonomous aircraft. Full article

This paper establishes a theoretical framework for treating communication quality as a navigable resource in long-endurance unmanned aerial vehicle (UAV) control under stochastic degradation. We prove that a hierarchical architecture integrating communication-aware model predictive control (MPC) achieves $\epsilon$-optimality with respect to the intractable stochastic dynamic programming formulation while maintaining exponential stability guarantees under switched system dynamics governed by continuous-time Markov chains. Three primary theoretical contributions were made: (1) A stochastic optimality theorem is given showing that sigmoid penalty function approximation yields bounded suboptimality of $\eta \le 0.12$ under mild ergodicity conditions; (2) a formal stability result for mode switching based on hysteresis was established using multiple Lyapunov functions, and it showed exponentially fast convergence with a decay rate of $\lambda \ge 0.23$; and (3) bifurcation analysis showed that there is a critical time threshold of 72 h at which thermal-induced gyro-drift in the GPS sensor causes a transition in navigation error dynamics from linear to catastrophic nonlinear growth. The validation through 2430 Monte Carlo missions over 54,686 flight hours resulted in an average increase in endurance by 243% (18.2 days versus 5.3 days), while keeping CEP at approximately 8.7 m and achieving 82% mission success under extreme communication degradation ($q_{comm}<0.3$). The statistical results confirm a very strong positive relationship between the Resilience Quotient (RQ) and the length of successful missions ($R^2=0.89$, $p<0.001$), supporting the theoretical model with empirical evidence. Full article

Mountainous megacities face a distinctive form of pluvial waterlogging in which terrain-controlled flow convergence, accelerating imperviousness, and aging drainage interact to produce chronic, spatially clustered failures rather than stochastic events. Existing frameworks, such as hydrodynamic modeling, data-driven machine learning, and multi-criteria composite indexing, carry distinctive failure modes at the municipal scale. This study develops and externally validates a city-wide, grid-based assessment framework for Chongqing, China, through three integrated choices. First, resilience is reformulated as a stabilized adaptation-to-risk ratio and subjected to an explicit falsification test against independent waterlogging observations. Second, multi-source hydroclimatic, topographic–hydrologic, land-cover, and service-accessibility indicators are integrated on a 500 m fishnet (22,500 cells) through within-component CRITIC–Entropy weighting and TOPSIS, with robustness diagnosed by a 500-iteration Monte Carlo weight-perturbation analysis. Third, a spatially grouped LightGBM classifier with SHAP interpretation serves both as an independent validation layer and as a mechanistic lens on non-linear driver thresholds. The composite risk surface achieves ROC-AUC values of 0.834 and 0.873 against two independent waterlogging registries, is strongly spatially clustered (Moran’s I = 0.81, p < 0.001), and preserves its ranking under aggressive weight perturbation (Spearman ρ ≥ 0.95 in 95% of scenarios). A counterintuitive finding emerges from the falsification test as resilience yields ROC-AUC below 0.5 on both point sets, indicating that accessibility-based capacity proxies systematically capture urban centrality rather than drainage robustness, like a diagnosable measurement problem affecting the wider resilience-index literature. LightGBM concentrates 88.0% of waterlogging cells within the top 10% of scored grids, and SHAP-derived thresholds align with saturation-ponding, well-drained, and convergence–hotspot regimes of classical hydrology. Together, these results reframe waterlogging assessment in complex terrain from a cartographic exercise into a falsifiable, resource-aware prioritization framework, and clarify why capacity maps and risk maps should be published as complementary instruments of flood governance. Full article

The quality of Global Navigation Satellite System (GNSS) observations on smartphones is highly susceptible to multipath and non-line-of-sight (NLOS) effects in urban environments, resulting in complex and highly variable observation errors. These challenges highlight the necessity of a reliable stochastic model to ensure robust and unbiased parameter estimation. However, conventional empirical stochastic models, such as elevation-dependent or signal-to-noise ratio (SNR)-based weighting schemes, are often insufficient to capture the rapidly changing stochastic behavior of observations in dense urban environments. To overcome this limitation, an adaptive GNSS stochastic model based on a deep neural network (DNN) is developed by integrating SNR, satellite elevation angle, and post-fit pseudorange residuals, which provide a strong indicator of observation quality and environmental context. Specifically, a fully connected DNN is designed to use SNR, satellite elevation angle, and post-fit pseudorange residual as input features, representing signal strength, satellite geometry, and residual information, respectively, and to learn their nonlinear relationship with measurement uncertainty. The network output is then used to adaptively update the diagonal elements of the measurement noise covariance matrix, thereby realizing epoch-wise adaptive weighting within the Kalman filtering process. The proposed DNN-based stochastic model, together with several conventional models, was evaluated using GNSS observations collected by a low-cost u-blox ZED-F9P receiver (u-blox AG, Thalwil, Switzerland) and a Samsung Galaxy S21+ smartphone (Samsung Electronics Co., Ltd., Suwon, Republic of Korea) during vehicle experiments in dense urban canyons. The code-based single point positioning (SPP) results demonstrate that the DNN-based model consistently outperforms traditional stochastic models under both open-sky and urban conditions. The improvement is particularly pronounced for smartphone observations in severely obstructed environments. The proposed DNN-based model reduces the 3D RMSE from 14.25 m, 13.68 m, and 13.05 m, obtained with the elevation-, SNR-, and integrated elevation–SNR-based models, respectively, to 8.94 m, representing an improvement of approximately 35%. A similar improvement is observed for the u-blox ZED-F9P receiver, where the 3D RMSE decreases from 5.71 m, 4.69 m, and 5.15 m to 3.10 m. These results suggest the effectiveness of the proposed DNN-based stochastic model in mitigating complex observation errors and improving positioning accuracy, providing a promising solution for reliable positioning of low-cost GNSS receivers in challenging urban environments. Full article