Search Results (203)

This study introduces the Hybrid Hydrological Forecast System (HHFS), a dual-stage, data-driven framework for monthly streamflow forecasting at the Santa Branca outlet in the upper Paraíba do Sul River Basin, Brazil. The system combines two nonlinear regressors, Multi-Layer Perceptron (MLP) and extreme Gradient Boosting (XGB), calibrated through a structured four-step evolutionary procedure in GA₁ (hydrological weighting, dual-regime Ridge fusion, rolling bias correction, and monthly mean–variance adjustment) and a hydro-adaptive probabilistic optimization in GA₂. SHAP-based analysis provides physical interpretability of the learned relations. The regressive stage (GA₁) generates a bias-corrected and climatologically consistent central forecast. After the full four-step optimization, GA₁ achieves robust generalization skill during the independent test period (2020–2023), yielding NSE = 0.77 ± 0.05, KGE = 0.85 ± 0.05, R² = 0.77 ± 0.05, and RMSE = 20.2 ± 3.1 m³ s⁻¹, representing a major improvement over raw MLP/XGB outputs (NSE ≈ 0.5). Time-series, scatter, and seasonal diagnostics confirm accurate reproduction of wet- and dry-season dynamics, absence of low-frequency drift, and preservation of seasonal variance. The probabilistic stage (GA₂) constructs a hydro-adaptive prediction interval whose width (max-min streamflow) and asymmetry evolve with seasonal hydrological regimes. The optimized configuration achieves comparative coverage COV = 0.86 ± 0.00, hit rate p = 0.96 ± 0.04, and relative width r = 2.40 ± 0.15, correctly expanding uncertainty during wet-season peaks and contracting during dry-season recessions. SHAP analysis reveals a coherent predictor hierarchy dominated by streamflow persistence, precipitation structure, temperature extremes, and evapotranspiration, jointly explaining most of the predictive variance. By combining regressive precision, probabilistic realism, and interpretability within a unified evolutionary architecture, the HHFS provides a transparent, physically grounded, and operationally robust tool for reservoir management, drought monitoring, and hydro-climatic early-warning systems in data-limited regions. Full article

Reliable state estimation in railway environments presents significant challenges due to geometric degeneracy resulting from repetitive structural layouts and point cloud sparsity caused by high-speed motion. Conventional LiDAR-based SLAM systems frequently suffer from longitudinal drift and mapping artifacts when operating in such feature-scarce and dynamically complex scenarios. To address these limitations, this paper proposes SERail-SLAM, a robust semantic-enhanced multi-sensor fusion framework that tightly couples LiDAR odometry, inertial pre-integration, and GNSS constraints. Unlike traditional approaches that rely on rigid voxel grids or binary semantic masking, we introduce a Semantic-Enhanced Adaptive Voxel Map. By leveraging eigen-decomposition of local point distributions, this mapping strategy dynamically preserves fine-grained stable structures while compressing redundant planar surfaces, thereby enhancing spatial descriptiveness. Furthermore, to mitigate the impact of environmental noise and segmentation uncertainty, a confidence-aware filtering mechanism is developed. This method utilizes raw segmentation probabilities to adaptively weight input measurements, effectively distinguishing reliable landmarks from clutter. Finally, a category-weighted joint optimization scheme is implemented, where feature associations are constrained by semantic stability priors, ensuring globally consistent localization. Extensive experiments in real-world railway datasets demonstrate that the proposed system achieves superior accuracy and robustness compared to state-of-the-art geometric and semantic SLAM methods. Full article

Unmanned Aerial Spraying Systems (UASS) has rapidly advanced precision crop protection. However, the spray performance of UASSs is influenced by nozzle atomization, rotor-induced airflow, and external environmental conditions. These factors cause strong spatiotemporal coupling and high uncertainty. As a result, visualization-based monitoring techniques are now essential for understanding these dynamics and supporting spray modeling and drift-mitigation design. This review highlights developments in spray visualization technologies along the “droplet–airflow–target” chain mechanism in UASS spraying. We first outline the physical fundamentals of droplet formation, liquid-sheet breakup, droplet size distribution, and transport mechanisms in rotor-induced flow. Dominant processes are identified across near-field, mid-field, and far-field scales. Next, we summarize major visualization methods. These include optical imaging (PDPA/PDIA, HSI, DIH), laser-based scattering and ranging (LD, LiDAR), and flow-field visualization (PIV). We compare their spatial resolution, measurement range, 3D reconstruction capabilities, and possible sources of error. We then review wind-tunnel trials, field experiments, and point-cloud reconstruction studies. These studies show how downwash flow and tip vortices affect plume structure, canopy disturbance, and deposition patterns. Finally, we discuss emerging intelligent analysis for large-scale monitoring—such as image-based droplet recognition, multimodal data fusion, and data-driven modeling. We outline future directions, including unified feature systems, vortex-coupled models, and embedded closed-loop spray control. This review is a comprehensive reference for advancing UASS analysis, drift assessment, spray optimization, and smart support systems. Full article

This study reports the thermodynamic performance of a patented compact vertical evaporator–absorber unit for LiBr–H₂O absorption cooling, extending Part I by translating validated prototype data into a rigorous control-volume assessment of coupled transport. Coolant-side calorimetry was used to determine the absorption heat-transfer rate (Q_abs), while a mass–energy balance provided an estimate of the absorption mass-transfer rate (${∆\dot{m}}_{abs}$) across twelve manually imposed thermal-load phases with tagged fan-OFF/ON sub-intervals. Linear trend (slope) analysis was applied to quantify phase-resolved dynamic behavior. Fan assistance produced three load-dependent regimes: (i) stabilization of downward trends under low and zero loads, yielding slope-based relative improvements above 100% in the most critical weak-gradient phases; (ii) acceleration of recovery at intermediate loads; and (iii) moderation of strongly positive drifts at high loads. The global thermal resistance ($R_{th}$) decreased by more than 30% in passive and low-load phases, and Wilcoxon signed-rank tests confirmed statistically significant reductions in most intervals (p < 0.05). Uncertainty contributions and robustness were quantified through an uncertainty budget decomposition and sensitivity analyses, and a subsystem-level normalization (η_EA = Q_abs/Q_in) is reported to support comparisons across loads without invoking cycle COP. Overall, active vapor-flow management using a low-power internal fan widens the useful operating envelope of compact absorbers and provides a validated thermodynamic baseline with practical, regime-aware control guidelines for decentralized low-carbon cooling technologies. Full article

In practical deployments of decentralized federated learning (FL) in Internet of Things (IoT) environments, the non-independent and identically distributed (Non-IID) nature of client-local data limits model performance. Furthermore, concept drift further exacerbates complexity and introduces temporal uncertainty that significantly degrades convergence and generalization. Existing approaches, which mainly rely on model-level similarity or static clustering, struggle to disentangle inherent data heterogeneity from dynamic distributional shifts, resulting in poor adaptability under drift scenarios. This paper proposes FedPLC, a novel FL framework that introduces two mechanism-level innovations: (i) Prototype-Anchored Representation Learning (PARL), a strategy inspired by Learning Vector Quantization (LVQ) that stabilizes the representation space against label noise and distributional shifts by aligning sample embeddings with class prototypes; and (ii) Label-wise Dynamic Community Adaptation (LDCA), a fine-grained adaptation mechanism that dynamically reorganizes classifier heads at the label level, enabling rapid personalization and drift-aware community evolution. Together, PARL and LDCA enable FedPLC to explicitly disentangle static Non-IID heterogeneity from temporal concept drift, achieving robust and fine-grained adaptation for large-scale IoT/edge client populations. Our experimental results on the Fashion-MNIST, CIFAR-10, and SVHN datasets demonstrate that FedPLC outperforms the state-of-the-art federated learning methods designed for concept drift in both abrupt drift and incremental drift scenarios. Full article

To address the need for robust investment strategies in an increasingly uncertain global market, this study focuses on an ambiguity-averse insurer facing exchange rate uncertainty while investing in a foreign stock market. The insurer’s surplus is modeled via a classical compound Poisson process, and exchange rate dynamics are captured using an Ornstein–Uhlenbeck process for the drift component. Within the framework of maximizing expected exponential utility of terminal wealth, we derive and solve the Hamilton–Jacobi–Bellman equation to characterize the optimal investment strategy and the associated value function. Finally, a numerical example illustrates how varying model parameters influences the insurer’s optimal investment behavior. Full article

Raw material price volatility, freight rates, and foreign exchange all pose significant uncertainty for lithium-ion battery manufacturers, jeopardising procurement planning and financial stability. In this paper, we formulate a discrete Conditional Value-at-Risk (CVaR) optimisation model to design implementable robust hedging strategies for multi-factor cost exposure. Unlike conventional continuous hedge models, which are often severely parameter-sensitive and require frequent rebalancing, the discrete approach takes hedge ratios to be fixed at a finite implementable grid (0%, 50%, 100%) and simultaneously minimises the expected cost and tail risk. We conduct two case studies: the first evaluates the model behaviour under stochastic price shocks using a multi-market simulation data set, and the second subjects the model to stress testing on correlation drift and tail amplification in order to examine systemic robustness. Our results show that, compared with an OLS-based hedge or a fully hedged benchmark, the discrete CVaR framework yields smoother hedge patterns, lower tail losses, and improved liquidity stability; in addition, our results indicate that, when combined with tail-risk penalisation, decision discretisation can endogenously confer robustness to the industrial procurement horizon. This work contributes to the stochastic optimisation literature and provides a practical tool for mitigating volatility in the global lithium supply chain. Full article

Semiconductor wafer manufacturing is one of the most complex and data-intensive processes in the industry, encompassing the front-end (FEOL), middle-end (MOL), and back-end (BEOL) stages, involving thousands of interdependent processes. Each stage can introduce potential variability, thereby reducing yield, making metrology and inspection crucial for process control. However, due to capacity, cost, and destructive testing constraints, exhaustive metrology for every wafer or die is impractical. Therefore, this study aims to introduce sampling strategies that have evolved to balance the accuracy, risk, and efficiency of measurement allocation. This review presents a literature review of static, adaptive, and dynamic sampling and discusses recent intelligent sampling techniques. The results show that traditional static sampling provides fixed, rule-based inspection schemes that ensure comparability and compliance but lack responsiveness to process variations. Adaptive sampling introduces flexibility, allowing measurement density to be adjusted based on detected drift, anomalies, or statistical control limits. Building on this, dynamic sampling represents a paradigm shift towards predictive, real-time decision-making driven by machine learning, risk analysis, and digital twin integration. The dynamic framework continuously assesses process uncertainties and prioritizes metrology to maximize information gain, thereby significantly reducing metrology workload without impacting yield or quality. Static, adaptive, and dynamic sampling together constitute a continuous evolution from deterministic control to self-optimizing intelligence. As semiconductor nodes move towards sub-3 nm, this intelligent sampling technology is crucial for maintaining yield, cost competitiveness, and process flexibility in autonomous, data-centric wafer fabs. Full article

Centro Español de Metrología (CEM) is developing a quantum frequency standard based on trapped calcium ions, marking its entry into the landscape of the second quantum revolution. Optical frequency standards offer unprecedented precision by referencing atomic transitions that are fundamentally stable and immune to environmental drift. However, the challenge of developing such a system from scratch is unaffordable for a medium-sized National Metrology Institute (NMI), which seems to limit the ability of an institute such as CEM to contribute to this field of research. To overcome this, CEM has adopted a hybrid strategy, combining commercially available components with custom integration to accelerate deployment. This paper defines and implements an architecture adapted to the constraints of a medium-size NMI, where the main contribution is the systematic design, selection, and interconnection of the subsystems required to realize this standard. The rationale behind the system design is presented, detailing the integration of key elements for ion trapping, laser stabilization, frequency measurement, and system control. Current progress, ongoing developments, and future research directions are outlined, establishing the foundation for spectroscopic measurements and uncertainty evaluation. The project represents a strategic step toward strengthening national capabilities in quantum metrology for a medium-sized NMI. Full article

Background/Objectives: Computational studies using drift diffusion models on go/no-go escape tasks consistently show that individuals with suicidal ideation (SI) preferentially engage in active escape from negative emotional states. This study extends these findings by examining how individuals with SI update beliefs about action–outcome contingencies and uncertainty when trying to escape an aversive state. Methods: Undergraduate students with (n = 58) and without (n = 62) a lifetime history of SI made active (go) or passive (no-go) choices in response to stimuli to escape or avoid an unpleasant state in a laboratory-based negative reinforcement task. A Hierarchical Gaussian Filter (HGF) was used to estimate trial-by-trial trajectories of contingency and volatility beliefs, along with their uncertainties, prediction errors (precision-weighted), and dynamic learning rates, as well as fixed parameters at the person level. Bayesian mixed-effects models were used to examine the relationship between trial number, SI history, trial type, and all two-way interactions on HGF parameters. Results: We did not find an effect of SI history, trial type, or their interactions on perceived volatility of reward contingencies. At the trial level, however, participants with a history of SI developed progressively stronger contingency beliefs while simultaneously perceiving the environment as increasingly stable compared to those without SI experiences. Despite this rigidity, they maintained higher uncertainty during escape trials. Participants with an SI history had higher dynamic learning rates during escape trials compared to those without SI experiences. Conclusions: Individuals with an SI history showed a combination of cognitive inflexibility and hyper-reactivity to prediction errors in escape-related contexts. This combination may help explain difficulties in adapting to changing environments and in regulating responses to stress, both of which are relevant for suicide risk. Full article

Biogas and biomethane represent promising domestic fuels compatible with decarbonization targets at a time when diversification of gas sources is essential due to market volatility and increasing security risks. In laboratory practice, however, biogas production is still frequently assessed manually, which increases measurement uncertainty, limits temporal resolution, and reduces comparability between experimental series. We present an open and low-cost platform for continuous monitoring based on the hydrostatic water-displacement principle, complemented by stabilized process conditions (temperature control at 37 °C with short-term variability of approximately ±0.02 °C), continuous measurement with a 1 Hz sampling rate, and cloud-based data visualization. The methodology builds on a standardized procedure grounded in well-defined pressure–height–volume conversion relationships and transparent signal processing, enabling objective comparison of substrates and experimental setups. Validation experiments confirmed the system’s capability to capture short-term transient phenomena, improve reproducibility among parallel reactors, and maintain long-term measurement stability. Long-duration tests demonstrated short-term scatter of approximately 0.06 mL, minimal drift below 0.15% per 24 h, and an expanded uncertainty of roughly 3.1% at 100 mL. In parallel BMP tests, the continuous method yielded final volumes 5.78% higher than the discrete pressure method, reflecting systematic bias introduced by sparse manual sampling and reactor handling. The basic configuration quantifies the cumulative volume and production rate of biogas and is readily extendable to online gas composition analysis. The proposed solution offers a replicable tool for research and education, reduces costs, supports measurement standardization, and accelerates the optimization and subsequent scale-up of biogas technologies toward pilot-scale and industrial applications. Full article

Sensor networks generate high-dimensional temporally dependent data across healthcare, environmental monitoring, and energy management, which demands robust machine learning for reliable forecasting. While gradient boosting methods have emerged as powerful tools for sensor-based regression, systematic evaluation under realistic deployment conditions remains limited. This work provides a comprehensive review and empirical benchmark of boosting algorithms spanning classical methods (AdaBoost and GBM), modern gradient boosting frameworks (XGBoost, LightGBM, and CatBoost), and adaptive extensions for streaming data and hybrid architectures. We conduct rigorous cross-domain evaluation on continuous glucose monitoring, urban air-quality forecasting, and building-energy prediction, assessing not only predictive accuracy but also robustness under sensor degradation, temporal generalization through proper time-series validation, feature-importance stability, and computational efficiency. Our analysis reveals fundamental trade-offs challenging conventional assumptions. Algorithmic sophistication yields diminishing returns when intrinsic predictability collapses due to exogenous forcing. Random cross-validation (CV) systematically overestimates performance through temporal leakage, with magnitudes varying substantially across domains. Calibration drift emerges as the dominant failure mode, causing catastrophic degradation across all the static models regardless of sophistication. Importantly, feature-importance stability does not guarantee predictive reliability. We synthesize the findings into actionable guidelines for algorithm selection, hyperparameter configuration, and deployment strategies while identifying critical open challenges, including uncertainty quantification, physics-informed architectures, and privacy-preserving distributed learning. Full article

The safe and reliable operation of energy storage stations critically depends on their thermal management systems, specifically the health states or working conditions of involved sensors, such as temperature, humidity, and pressure sensor. Impacted by several environmental factors, some indiscernible defects including signal drift, elevated noise, and response lag may affect the exact surveillance of batteries, leading to potential combustion or even explosion, which requires fault risk early-warning to support timely maintenance. These multi-sensor environmental factor data typically exhibit mixed characteristics, component coupling, and high uncertainty, thus impacting diagnostic accuracy and robustness. With this motivation, this study proposes a pattern-guided framework for vulnerability diagnosis using Component Importance Measure. A pattern-guided strategy is first designed to perform rule induction and fuzzy processing on discrete and continuous sensor data, respectively, to extract underlying vulnerability-related components. Subsequently, a component Importance Measure, which assesses the impact of individual risks on the whole reliability, is established to achieve unified integration and mapping of previous heterogeneous information, therefore providing multidimensional vulnerability representations. An empirical case study demonstrates the fault detection rate, false alarm control, and diagnostic stability of the proposed framework. Full article

In Global Navigation Satellite System (GNSS)-denied environments, reconstructing three dimensional trajectories using only an Inertial Measurement Unit faces challenges such as heading drift, stride error accumulation, and gait recognition uncertainty. This paper proposes a path estimation method with a nine-axis inertial sensor that continuously and accurately estimates an agent’s path without external support. The method detects stationary states and halts updates to suppress error propagation. During motion, gait modes including flat walking, stair ascent, and stair descent are classified using vertical acceleration with dynamic thresholds. Vertical displacement is estimated by combining gait pattern and posture angle during stair traversal, while planar displacement is updated through adaptive stride length adjustment based on gait cycle and movement magnitude. Heading is derived from the attitude matrix aligned with magnetic north, enabling projection of displacements onto a unified frame. Experiments show planar errors below three percent for one-hundred-meter paths and vertical errors under two percent in stair environments up to ten stories, with stable heading maintained. Overall, the method achieves reliable gait recognition and continuous three-dimensional trajectory reconstruction with low computational cost, using only a single inertial sensor and no additional devices. Full article

Maritime search and rescue operations rely on accurate drift predictions to define effective search areas for missing persons. Existing systems often depict uncertainty using statistical ellipses or ensemble-based probability maps, which may not effectively capture directional biases and underlying flow structures. In this study, we introduce a geometric framework that constructs possible object trajectories directly from the drift dynamics. Starting from the last known position, we integrate the translational and rotational drift components with arbitrary perturbations to model realistic scenarios. The resulting envelope of the trajectories defines a reachable set that adapts to the flow without relying on sampling or covariance estimations. Using satellite-derived wind and current data, we demonstrat that this approach produces envelopes that are physically consistent and operationally relevant. Our method offers a mathematically grounded alternative to ensemble techniques, enhancing interpretability and improving the SAR planning efficiency. We illustrate its effectiveness with examples that simulate real-world scenarios. Full article