Search Results (6,525)

Artificial intelligence (AI) techniques have propelled biomedical sensors beyond measuring physiological markers to interpreting subjective states like stress, pain, or emotions. Despite these technological advances, user trust is not guaranteed and is inadequately addressed in extant research. This review proposes the Cognition–Context–Contrast (CCC) conceptual framework to explain the trust and acceptance of AI-enabled sensors. First, we map cognition, comprising the expectations and stereotypes that humans have about machines. Second, we integrate task context by situating sensor applications along an intellective-to-judgmental continuum and showing how demonstrability predicts tolerance for sensor uncertainty and/or errors. Third, we analyze contrast effects that arise when automated sensing displaces familiar human routines, heightening scrutiny and accelerating rejection if roll-out is abrupt. We then derive practical implications such as enhancing interpretability, tailoring data presentations to task demonstrability, and implementing transitional introduction phases. The framework offers researchers, engineers, and clinicians a structured conceptual framework for designing and implementing the next generation of AI biosensors. Full article

In the process of plant protection for fruit trees using rotary-wing UAVs, challenges such as droplet drift, insufficient canopy penetration, and low agrochemical utilization efficiency remain prominent. Among these, the uncertainty in the spatio-temporal distribution of downwash airflow is a key factor contributing to non-uniform droplet deposition and increased drift. To address this issue, we developed a wind field numerical simulation model based on an improved hierarchical leaf density model to clarify the spatio-temporal characteristics of downwash airflow, the scale of turbulence regions, and their effects on internal canopy airflow under varying flight altitudes and different rotor speeds. Field experiments were conducted in orchards to validate the accuracy of the model. Simulation results showed that the average error between the simulated and measured wind speeds inside the canopy was 8.4%, representing a 42.11% reduction compared to the non-hierarchical model and significantly improving the prediction accuracy. The coefficient of variation (CV) was 0.26 in the middle canopy layer and 0.29 in the lower layer, indicating a decreasing trend with an increasing canopy height. We systematically analyzed the variation in turbulence region scales under different flight conditions. This study provides theoretical support for optimizing UAV operation parameters to improve droplet deposition uniformity and enhance agrochemical utilization efficiency. Full article

Articulated arm coordinate measuring machines are designed for in situ use directly in manufacturing environments, enabling efficient dimensional control outside of climate-controlled laboratories. This study investigates the influence of ambient temperature variation on the accuracy of length measurements performed with the Hexagon Absolute Arm 8312. The experiment was carried out in a laboratory setting simulating typical shop floor conditions through controlled temperature changes in the range of approximately 20–31 °C. A calibrated steel gauge block was used as a reference standard, allowing separation of the influence of the measuring system from that of the measured object. The results showed that the gauge block length changed in line with the expected thermal expansion, while the articulated arm coordinate measuring machine exhibited only a minor residual thermal drift and stable performance. The experiment also revealed a constant measurement offset of approximately 22 µm, likely due to calibration deviation. As part of the study, an uncertainty budget was developed, taking into account all relevant sources of influence and enabling a more realistic estimation of accuracy under operational conditions. The study confirms that modern carbon composite articulated arm coordinate measuring machines with integrated compensation can maintain stable measurement behavior even under fluctuating temperatures in controlled environments. Full article

Forestry carbon sinks play a crucial role in mitigating climate change and protecting ecosystems, significantly contributing to the development of carbon trading systems. Remote sensing technology has become increasingly important for monitoring carbon sinks, as it allows for precise measurement of carbon storage and ecological changes, which are vital for forecasting carbon prices. Carbon prices fluctuate due to the interaction of various factors, exhibiting non-stationary characteristics and inherent uncertainties, making accurate predictions particularly challenging. To address these complexities, this study proposes a method for predicting carbon trading prices influenced by multiple factors. We introduce a Decomposition (DECOMP) module that separates carbon price data and its influencing factors into trend and cyclical components. To manage non-stationarity, we propose the KAN with Multi-Domain Diffusion (KAN-MD) module, which efficiently extracts relevant features. Furthermore, a Wave-MH attention module, based on wavelet transformation, is introduced to minimize interference from uncertainties, thereby enhancing the robustness of the model. Empirical research using data from the Hubei carbon trading market demonstrates that our model achieves superior predictive accuracy and resilience to fluctuations compared to other benchmark methods, with an MSE of 0.204% and an MAE of 0.0277. These results provide reliable support for pricing carbon financial derivatives and managing associated risks. Full article

We introduce a novel portfolio optimization framework—Distributionally Robust Multivariate Stochastic Cone Order (DR-MSCO)—which integrates partial orders on random vectors with Wasserstein-metric ambiguity sets and adaptive cone structures to model multivariate investor preferences under distributional uncertainty. Grounded in measure theory and convex analysis, DR-MSCO employs data-driven cone selection calibrated to market regimes, along with coherent tail-risk operators that generalize Conditional Value-at-Risk to the multivariate setting. We derive a tractable second-order cone programming reformulation and demonstrate statistical consistency under empirical ambiguity sets. Empirically, we apply DR-MSCO to 23 Borsa Istanbul equities from 2021–2024, using a rolling estimation window and realistic transaction costs. Compared to classical mean–variance and standard distributionally robust benchmarks, DR-MSCO achieves higher overall and crisis-period Sharpe ratios (2.18 vs. 2.09 full sample; 0.95 vs. 0.69 during crises), reduces maximum drawdown by 10%, and yields endogenous diversification without exogenous constraints. Our results underscore the practical benefits of combining multivariate preference modeling with distributional robustness, offering institutional investors a tractable tool for resilient portfolio construction in volatile emerging markets. Full article

This paper presents a Systematic Mapping Study (SMS) on data-driven approaches in flood forecasting from 2019 to 2024, a period marked by transformative developments in Deep Learning (DL) technologies. Analysing 363 selected studies, this paper provides an overview of the technological evolution in this field, methodological approaches, evaluation practices and geographical distribution of studies. The study revealed that meteorological and hydrological factors constitute approximately 76% of input variables, with rainfall/precipitation and water level measurements forming the core predictive basis. Long Short-Term Memory (LSTM) networks emerged as the dominant algorithm (21% of implementations), whilst hybrid and ensemble approaches showed the most dramatic growth (from 2% in 2019 to 10% in 2024). The study also revealed a threefold increase in publications during this period, with significant geographical concentration in East and Southeast Asia (56% of studies), particularly China (36%). Several research gaps were identified, including limited exploration of graph-based approaches for modelling spatial relationships, underutilisation of transfer learning for data-scarce regions, and insufficient uncertainty quantification. This SMS provides researchers and practitioners with actionable insights into current trends, methodological practices, and future directions in data-driven flood forecasting, thereby advancing this critical field for disaster management. Full article

Autonomous underwater vehicles (AUVs) have obtained extensive application in the exploitation of marine resources. Terrain-aided navigation (TAN), as an accurate and reliable autonomous navigation method, is commonly used for AUV navigation. However, its accuracy degrades significantly in self-similar terrain features or measurement uncertainties. To overcome these challenges, we propose a novel terrain-aided navigation framework integrating an Improved Marine Predators Algorithm with Depth-First Search optimization (DFS-IMPA-TAN). This framework maintains positioning precision in partially self-similar terrains through two synergistic mechanisms: (1) IMPA-driven optimization based on the hunger-inspired adaptive exploitation to determine optimal trajectory transformations, cascaded with Kalman filtering for navigation state correction; (2) a Robust Tree (RT) hypothesis manager that maintains potential trajectory candidates in graph-structured memory, employing Depth-First Search for ambiguity resolution in feature matching. Experimental validation through simulations and in-vehicle testing demonstrates the framework’s distinctive advantages: (1) consistent terrain association in partially self-similar topographies; (2) inherent error resilience against ambiguous feature measurements; and (3) long-term navigation stability. In all experimental groups, the root mean squared error of the framework remained around 60 m. Under adverse conditions, its navigation accuracy improved by over 30% compared to other traditional batch processing TAN methods. Comparative analysis confirms superior performance over conventional methods under challenging conditions, establishing DFS-IMPA-TAN as a robust navigation solution for AUVs in complex underwater environments. Full article

Background/Objective: Food insecurity refers to social or economic challenges that limit or create uncertainty around access to enough food. Among college students, food security status is usually determined with the USDA 10-item Food Security Survey Module, which has not been validated for this population. Nutrition security refers to consistent access to food and beverages that promote well-being, prevent disease, and emphasize equitable access to healthy, safe, and affordable foods. Currently, there is no standardized measure that assesses food and nutrition security tailored to the unique experiences of college students. This study aims to evaluate the validity and reliability of a newly developed College Student Food and Nutrition Security Survey Module (CS-FNSSM). Methods: A mixed-methods approach that combined an online survey with semi-structured cognitive interviews. Participants were students aged 18 and older from U.S. public universities. Quantitative data were analyzed using RStudio (version 4.4.1), and interview transcripts were thematically analyzed. Results: Survey responses were collected from 953 participants, including a subset of 69 participants for reliability testing and 30 participants for cognitive interviews. Rasch analysis showed good item performance and structural validity. The CS-FNSSM demonstrated strong sensitivity (89.09%), specificity (76.2%), moderate test–retest reliability (0.59), and good internal consistency (Cronbach’s alpha = 0.79). Qualitative findings confirmed participant understanding of the items. Conclusions: The CS-FNSSM effectively identifies food and nutrition insecurity, with nutrition security emerging as a key issue. Addressing both is crucial for promoting the overall health and well-being of college students. Full article

The transition to sustainable agriculture is a critical challenge for the U.S. food system. A sustainable food system must support the production of healthy and nutritious food while ensuring economic sustainability for farmers and ranchers. It should also reduce negative environmental impacts on soil, water, biodiversity, and climate, and promote equitable and inclusive access to land, farming resources, and food. This narrative review synthesizes U.S. social science literature to identify the key factors that support or impede the adoption of sustainable agricultural practices in the U.S. Our analysis reveals seven overarching factors that influence producer decision-making: awareness and knowledge, social factors, psychological factors, technologies and tools, economic factors, implementation capacity, and policies and regulations. The review highlights the critical role of social science in navigating complexity and uncertainty. Key priorities emerging from the literature include developing measurable, outcome-based programs; ensuring credible communication through trusted intermediaries; and designing tailored interventions. The findings demonstrate that initiatives will succeed when they emphasize measurable benefits, address uncertainties, and develop programs that capitalize on identified opportunities while overcoming existing barriers. Full article

Accurate localization plays a critical role in enabling underwater vehicle autonomy. In this work, we develop a robust infrastructure-based localization framework that estimates the position and orientation of underwater vehicles using only range measurements from long baseline (LBL) acoustic beacons to multiple on-board receivers. The proposed framework integrates three key components, each formulated as a convex optimization problem. First, we introduce a robust calibration function that unifies multiple sources of measurement error—such as range-dependent degradation, variable sound speed, and latency—by modeling them through a monotonic function. This function bounds the true distance and defines a convex feasible set for each receiver location. Next, we estimate the receiver positions as the center of this feasible region, using two notions of centrality: the Chebyshev center and the maximum volume inscribed ellipsoid (MVE), both formulated as convex programs. Finally, we recover the vehicle’s full 6-DOF pose by enforcing rigid-body constraints on the estimated receiver positions. To do this, we leverage the known geometric configuration of the receivers in the vehicle and solve the Orthogonal Procrustes Problem to compute the rotation matrix that best aligns the estimated and known configurations, thereby correcting the position estimates and determining the vehicle orientation. We evaluate the proposed method through both numerical simulations and field experiments. To further enhance robustness under real-world conditions, we model beacon-location uncertainty—due to mooring slack and water currents—as bounded spherical regions around nominal beacon positions. We then mitigate the uncertainty by integrating the modified range constraints into the MVE position estimation formulation, ensuring reliable localization even under infrastructure drift. Full article

Accurate methane emission estimates are essential for climate policy, yet current field methods often struggle with spatial constraints and source complexity. Ground-based mobile approaches frequently miss key plume features, introducing bias and uncertainty in emission rate estimates. This study addresses these limitations by using small unmanned aerial systems equipped with precision gas sensors to measure methane alongside co-released tracers. We tested whether arc-shaped flight paths and alternative ratio estimation methods could improve the accuracy of tracer-based emission quantification under real-world constraints. Controlled releases using ethane and nitrous oxide tracers showed that (1) arc flights provided stronger plume capture and higher correlation between methane and tracer concentrations than traditional flight paths; (2) the cumulative sum method yielded the lowest relative error (as low as 3.3%) under ideal mixing conditions; and (3) the arc flight pattern yielded the lowest relative error and uncertainty across all experimental configurations, demonstrating its robustness for quantifying methane emissions from downwind plume measurements. These findings demonstrate a practical and scalable approach to reducing uncertainty in methane quantification. The method is well-suited for challenging environments and lays the groundwork for future applications at the facility scale. Full article

Downhole kick is one of the most severe safety hazards in deep and ultra-deep well drilling operations. Traditional monitoring methods, which rely on surface flow rate and fluid level changes, are limited by their delayed response and insufficient sensitivity, making them inadequate for early warning. This study proposes a real-time monitoring technique for gas content in drilling fluid based on the attenuation principle of Ba-133 γ-rays. By integrating laboratory static/dynamic experiments and Geant4-11.2 Monte Carlo simulations, the influence mechanism of gas–liquid two-phase media on γ-ray transmission characteristics is systematically elucidated. Firstly, through a comparative analysis of radioactive source parameters such as Am-241 and Cs-137, Ba-133 (main peak at 356 keV, half-life of 10.6 years) is identified as the optimal downhole nuclear measurement source based on a comparative analysis of penetration capability, detection efficiency, and regulatory compliance. Compared to alternative sources, Ba-133 provides an optimal energy range for detecting drilling fluid density variations, while also meeting exemption activity limits (1 × 10⁶ Bq) for field deployment. Subsequently, an experimental setup with drilling fluids of varying densities (1.2–1.8 g/cm³) is constructed to quantify the inverse square attenuation relationship between source-to-detector distance and counting rate, and to acquire counting data over the full gas content range (0–100%). The Monte Carlo simulation results exhibit a mean relative error of 5.01% compared to the experimental data, validating the physical correctness of the model. On this basis, a nonlinear inversion model coupling a first-order density term with a cubic gas content term is proposed, achieving a mean absolute percentage error of 2.3% across the full range and R² = 0.999. Geant4-based simulation validation demonstrates that this technique can achieve a measurement accuracy of ±2.5% for gas content within the range of 0–100% (at a 95% confidence interval). The anticipated field accuracy of ±5% is estimated by accounting for additional uncertainties due to temperature effects, vibration, and mud composition variations under downhole conditions, significantly outperforming current surface monitoring methods. This enables the high-frequency, high-precision early detection of kick events during the shut-in period. The present study provides both theoretical and technical support for the engineering application of nuclear measurement techniques in well control safety. Full article

In response to the demand for precise measurement of illuminance distribution in the quality control of LED monitoring fill light products and the iterative direction of secondary optical design, distributed photometric sensors have shown advantages, but their measurement uncertainty assessment faces challenges. This paper addresses the problem of uncertainty evaluation in photometric parameter measurement with a two-dimensional horizontal distributed photometric sensor and proposes an uncertainty evaluation framework for this task. We have established an uncertainty analysis model for the measurement system and provided two uncertainty synthesis methods, The Guide to the Expression of Uncertainty in Measurement and the Monte Carlo method. This study designed illuminance measurement experiments to validate the feasibility of the proposed uncertainty evaluation method. The results demonstrate that the actual probability distribution of the measurement data follows a trapezoidal distribution. Furthermore, the expanded uncertainty calculated using the GUM method was 21.1% higher than that obtained by the MCM. This work effectively addresses the uncertainty evaluation challenge for illuminance measurement tasks using a two-dimensional horizontal distributed photometric sensor. The findings offer valuable reference for the uncertainty assessment of other high-precision optical instruments and possess significant engineering value in enhancing the reliability of optical metrology systems. Full article

The investigations examined a potential reduction in discrepancies between the values of the unperturbed radiofrequency (RF) electromagnetic field (EMF) and values of the EMF measured by wearable equipment (personal exposure meters) impacted by the proximity of the human body. This was done by modelling distributed wearable (multi-location, with up to seven simultaneously locations) measurements. The performed numerical simulations mimicked distributed measurements in 24 environmental exposure scenarios (recognized as virtual measurements) covered: the horizontal or vertical propagation of the EMF and electric field vector polarization corresponding to typical conditions of far-field exposure from wireless communication systems (at a frequency of 100–3600 MHz). Physical tests using three EMF probes for simultaneous measurements have been also performed. Studies showed that the discrepancy in assessing EMF exposure by an on-body equipment and the parameters of the unperturbed EMF in the location under inspection (mimicking the contribution to measurement uncertainty from the human body proximity) may be significantly reduced by the appropriate use of a distributed measurement system. The use of averaged values, from at least three simultaneous measurements at relevant locations on the body, may reduce the uncertainty approximately threefold. Full article

Developing and identifying the correct reactor model for a reaction system characterized by a high number of reaction pathways and flow regimes can be challenging. In this work, artificial neural networks (ANNs), used in deep learning, are used to develop a hybrid modelling framework for physics-based model discovery in reactions systems. The model discovery accuracy of the framework is investigated considering kinetic model parametric uncertainty, noise level, features in the data structure and experimental design optimization via a differential evolution algorithm (DEA). The hydrodynamic behaviours of both a continuously stirred tank reactor and a plug flow reactor and rival chemical kinetics models are combined to generate candidate physics-based models to describe a benzoic acid esterification synthesis in a rotating cylindrical reactor. ANNs are trained and validated from in silico data simulated by sampling the parameter space of the physics-based models. Results show that, when monitored using test data classification accuracy, ANN performance improved when the kinetic parameters uncertainty decreased. The performance improved further by increasing the number of features in the data set, optimizing the experimental design and decreasing the measurements error (low noise level). Full article