Search Results (22,877)

Accurate freeway Improvements in traffic state prediction accuracy and enhanced stability enable more proactive traffic control and demand management strategies, thereby reducing congestion spillover effects, unnecessary acceleration–deceleration cycles, and the resulting fuel consumption and emissions. Yet, this remains challenging due to the interplay between deterministic traffic flow mechanisms and stochastic disturbances. Purely data-driven models suffer from error accumulation under out-of-distribution conditions, while physics-based models lack flexibility in capturing nonlinear deviations. This paper proposes MDURP, a physics-constrained residual learning framework that reformulates prediction as a residual-space learning problem. A calibrated Cell Transmission Model generates a physically admissible baseline; deep learning models are then restricted to learning the residuals. Wavelet decomposition and GARCH volatility modeling address the multi-scale and heteroskedastic characteristics of these residuals. Experimental results demonstrate that MDURP consistently outperforms baseline models, reducing MAE by an average of 6.8%, RMSE by an average of 4%. The framework also suppresses long-term error accumulation, with MAPE escalation slowing from 0.79% to 0.58% per step. These gains confirm that anchoring deep learning within a physics-defined residual space enhances both accuracy and stability. Full article

Accurate soil moisture assessment is essential for effective agricultural management in the southern US, where water availability has a significant impact on crop productivity. This study evaluates the Soil Moisture Active Passive (SMAP) Level-4 daily soil moisture product using in situ measurements from Natural Resources Conservation Service (NRCS) Soil Climate Analysis Network (SCAN) stations and the US. Climate Reference Network (USCRN) across diverse agroecosystems in Texas from 2016 to 2024. SMAP’s performance was examined across ten climate zones and six major land cover types, including urban regions, pastureland, grassland, rangeland, shrubland, and deciduous forests. Statistical metrics, including the coefficient of determination (R²), Root Mean Square Error (RMSE), Bias, and unbiased RMSE (ubRMSE) were used to evaluate the agreement between SMAP-derived and in situ soil moisture measurements. Results show that SMAP effectively captures seasonal soil moisture dynamics but exhibits spatially variable accuracy. The highest agreement was observed at Panther Junction (R² = 0.57, RMSE = 2.29%), followed by Austin (R² = 0.57, RMSE = 9.95%). While a weaker coefficient of determination was observed at PVAMU (R² = 0.28, RMSE = 11.28%) and Kingsville (R² = 0.11, RMSE = 7.33%), likely due to heterogeneity in land cover, and urbanized landscapes in these stations. Applying the quantile mapping bias correction methods significantly reduced RMSE and improved the accuracy of SMAP soil moisture data at some in situ measurement stations. The results highlight the importance of station-specific calibration and the integration of satellite and ground-based measurements to improve soil moisture monitoring for agriculture and drought management in Texas and similar regions. Full article

Ultra-High-Performance Concrete (UHPC) is increasingly used in slender and prestressed structural members due to its superior strength and durability. However, inaccurate or incomplete prediction of creep deformation may lead to excessive long-term deflection, prestress loss, cracking, and potential serviceability or safety risks in buildings and infrastructure. Therefore, reliable prediction methods for UHPC creep are essential for both structural design and long-term performance assessment. In this study, a database containing 60 literature-derived UHPC creep records was compiled to investigate the creep coefficient at approximately 100 days. Pearson correlation analysis revealed strong interdependence among predictors and weak single-variable linear relationships, indicating that creep behavior is governed by nonlinear interactions. A feedforward backpropagation neural network (BPNN) trained using the Levenberg–Marquardt algorithm was developed to predict the creep coefficient. To maintain engineering interpretability, the fib Model Code 2020 (MC2020) formulation was adopted as a code-based benchmark and further calibrated using ridge regression. Results show that the calibrated MC2020 model improves prediction consistency, while the BPNN model provides the highest predictive accuracy. The proposed framework integrates machine-learning prediction with interpretable code-based calibration, contributing to the development of creep modeling approaches for UHPC and providing practical support for the safe design of UHPC structures. Full article

To address the high hardware costs associated with hyperspectral imaging in precision agriculture, spectral reconstruction (SR) is emerging as a feasible solution for obtaining hyperspectral images. However, existing methods, mainly including CNN and Transformer, face a notable dilemma: convolutional neural networks (CNNs) are limited by their local receptive fields, while Transformers encounter the problem of quadratic computational complexity. Effectively balancing computational efficiency with the capture of long-range spatial dependencies remains a significant challenge. To this end, this study proposes FGA-Mamba (Frequency-Gradient Attention Mamba), a novel reconstruction network based on the Mamba architecture. This network introduces a Frequency-Visual State Space (F-VSS) module, which combines the linear long-range modeling capability of state space models (SSMs) with a frequency-domain self-calibration mechanism to enhance global structural consistency by explicitly modulating frequency features. In addition, we designed an Enhanced Gradient Attention Module (EGAM). This module optimizes local feature representation through a gradient-aware mechanism, effectively compensating for the loss of spatial details. Experimental results on 3 datasets shows that FGA-Mamba have significant improvement in both quantitative and qualitative metrics. Moreover, the high consistency observed in vegetation index (VI) calculations confirms its potential for practical agricultural application. Full article

Dual-band weather radar networks enable complementary multi-radar observations, improving the accuracy, three-dimensional characterization, and early warning capability for severe convective weather. S-band radar provides strong penetration and long detection range but suffers from limited spatial resolution, whereas X-band radar offers high resolution with weaker penetration, posing challenges for dual-frequency data fusion. To address the resolution mismatch and fusion modeling issues between dual-band radars, this study proposes a super-resolution reconstruction method for S-band reflectivity based on dual-frequency radar observations. S-band and X-band radar data, together with key polarimetric parameters, are jointly incorporated into a deep neural network-based fusion model to enhance the spatial resolution of S-band reflectivity. Experimental results under typical severe weather conditions demonstrate that the proposed method achieves improved detail recovery and structural reconstruction, with the model achieving PSNR 30.84, SSIM 0.8755, and MAE 0.24178, which shows obvious advantages compared with other models and effectively enhances radar network data quality, and it outperforms single S-band super-resolution approaches in both objective metrics and subjective evaluations. Full article

This study employed near-infrared (NIR) spectroscopy for real-time spectral acquisition of fermentation broth in lab-scale bioreactors, comparing the performance of transflectance and transmission sensors through glucose modeling and prediction while optimizing modeling approaches. The results demonstrated superior adaptability of transflectance sensors in fermentation environments: in conventional fermentation, glucose models exhibited lower errors (RMSEC = 4.087 g/L, RMSEV = 9.829 g/L) compared to transmission sensors (RMSEC = 5.972 g/L, RMSEV = 10.904 g/L), with significantly higher predictive performance (RPD = 3.735 vs. 2.369), indicating enhanced fitting accuracy and stability. In complex natural media containing peptone and yeast extract, transmission sensor performance deteriorated dramatically due to turbidity interference (R²_cal = 0.134), whereas transflectance sensors maintained robust performance (R²_cal = 0.993), confirming their adaptability to complex matrices. Regarding modeling strategies, the 1550–1700 nm spectral region demonstrated optimal feature extraction capability (RMSEC = 3.269 g/L, R²_cal = 0.987). Basic preprocessing methods such as the moving average smoothing method have become the preferred preprocessing methods, as they strike a balance between calibration and prediction performance. Outlier removal analysis revealed that moderate elimination of 12 high-error samples (accounting for 30% of the total 39 samples) reduced RMSEC to 1.441 g/L and improved R²_cv to 0.996, optimizing model performance; however, excessive removal of outlier samples degraded model capability, necessitating judicious sample selection. For fixed total sample sizes, calibration sets comprising 70–80% of samples yielded more reliable predictions. In conclusion, transflectance sensors demonstrate superior compatibility with multicomponent fermentation systems. Combined with wavelength selection, moving average preprocessing, and rational sample removal and partitioning strategies, this approach provides an effective solution for NIR-based online glucose monitoring. Full article

Visual perception is fundamental to robotic manipulation for recognizing objects, goals, and contextual details. Third-person cameras provide global views but can miss contact-rich interactions and require calibration. Wrist-mounted egocentric cameras reduce these limitations but introduce occlusion, motion blur, and partial observability, which complicate visuomotor learning. Furthermore, existing perception modules that rely solely on pixels or fuse imagery with proprioception as flat vectors do not explicitly model structured scene representations in dynamic egocentric views. To address these challenges, a multi-slot attention fusion encoder for egocentric manipulation is introduced. Learnable slot queries extract localized visual features from image tokens, and Feature-wise Linear Modulation (FiLM) conditions each slot on the robot’s joint states, producing a structured slot-based latent representation that adapts to viewpoint and configuration changes without requiring object labels or external camera priors. The resulting structured slot-based latent representation is used as input to a Soft Actor–Critic (SAC) agent, which achieves a higher mean cumulative return than pixel-only CNN/DrQ and state-only baselines on a ManiSkill3 egocentric manipulation task. Probing experiments and real-camera evaluation further show that the learned representation remains stable under egocentric viewpoint shifts and partial occlusions, indicating robustness in practical manipulation settings. Full article

Long-distance coal gangue slurry transportation pipelines are critical components of underground coal mine green backfilling systems, yet blockage failures severely threaten their safe and efficient operation. Existing distributed acoustic sensing (DAS)-based monitoring methods for such pipelines suffer from three key limitations: insufficient fixed-point quantitative accuracy, lack of verified blockage-specific characteristic indicators, and limited quantitative severity assessment capability. To address these gaps, this paper proposes a novel feature-level fusion monitoring method integrating DAS, fiber Bragg grating (FBG), and piezoelectric accelerometers for accurate blockage identification and quantitative evaluation in coal gangue slurry pipelines. A slurry pipeline circulation test platform with gradient blockage simulation (0% to 76.42%) and a synchronous multi-sensor monitoring system were developed. Through multi-domain signal analysis, three blockage-correlated characteristic frequencies were identified and cross-validated by synchronous multi-sensor data: 1.5 Hz (system background vibration), 26 Hz (blockage-induced fluid–structure resonance, verified by the Euler–Bernoulli beam theory with a theoretical value of 25.7 Hz), and 174 Hz (transient flow impact). The DAS phase change rate exhibited a unimodal nonlinear response to blockage degree, with the peak occurring at 40.94% blockage. On this basis, a sine-fitting quantitative inversion model was developed, achieving a high goodness of fit (R² = 0.985), and leave-one-out cross-validation confirmed its excellent robustness with a mean relative prediction error of 3.77%. Finally, a collaborative monitoring framework was built to fully leverage the complementary advantages of each sensor, realizing full-process blockage monitoring covering global blockage localization, precise quantitative severity calibration, and high-frequency transient risk early warning. The proposed method provides a robust experimental and technical foundation for real-time early warning, precise localization, and quantitative diagnosis of long-distance slurry pipeline blockages and holds important engineering application value for the safe and efficient operation of underground coal mine green backfilling systems. Full article

The accurate inference of hidden states from non-stationary physiological signals remains a significant challenge in stochastic process modeling. This paper proposes an Adaptive Sticky Hidden Markov Model (Sticky-HMM) framework designed to enhance the robustness of state decoding in noisy environments. To address the “state-flickering” issue inherent in traditional HMMs, we incorporate a “Sticky” parameter into the transition matrix, imposing a temporal penalty on spurious state switching to maintain continuity. Furthermore, we introduce a Dynamic Prior Strategy that adaptively calibrates self-transition probabilities by mapping frequency-domain features of the observed sequence to the model’s parameter space. The proposed decoding process employs a two-pass refinement strategy and the Viterbi algorithm in the logarithmic domain to ensure numerical stability. The model’s efficacy was validated using a high-fidelity dataset of simulated apnea events. This work provides a computationally efficient and mathematically rigorous approach that demonstrates strong potential for long-term respiratory health monitoring. Full article

About 10% of plastic products are recycled worldwide, highlighting the need for technology improvements based on deeper material understanding. In packaging, which holds the highest market share in plastics demand, odor and potential hazards remain critical barriers to high-quality recycling. Conventional characterization relies on chromatography with extensive sample preparation. A gas chromatography system equipped with thermal desorption and dual flame ionization and mass spectrometric detection (ATD-GC/FID-MS) was established to analyze recyclates directly, thereby accelerating technology adaptation and guiding follow-up analyses. For calibration and validation, liquid standards were introduced into TenaxTA-filled tubes via a packed column injector and compared to a loading rig. The injector exhibited losses for higher-molar-mass compounds and solvent-dependent signal shifts. A storage study on compounded recycled polypropylene stored under various conditions showed that samples not frozen in sealed containers should be analyzed within 30 days. Experiments with varying sample geometries demonstrated that higher surface-to-volume ratios increase volatile release and variability in results, highlighting the need for uniform shapes. Applying the method to recycled yogurt cups enables the identification and quantification of contaminants, facilitating optimization of the washing process. Overall, ATD-GC/FID-MS provides a rapid screening tool for recyclate quality control and supports the improvement of recycling technologies. Full article

The growing demand for high-fidelity, multi-parameter, distributed sensing in critical domains such as structural health monitoring, oil and gas exploration, and secure perimeter surveillance is pushing traditional optical fiber sensors (OFS) to their performance limits. Although conventional multiplexing techniques such as time-division and wavelength-division multiplexing (TDM, WDM) have been commercially successful, they are rapidly approaching fundamental bottlenecks in sensor density, spatial resolution, and data capacity. This review argues that the synergistic convergence of space-division multiplexing (SDM) and artificial intelligence (AI) represents a paradigm shift, enabling a new generation of intelligent, high-dimensional sensing networks. We comprehensively survey the state of the art in SDM-based OFS, detailing the operating principles and applications of multi-core fibers (MCFs) for ultra-dense sensor arrays and 3D shape sensing, as well as few-mode fibers (FMFs) for mode-division multiplexing and enhanced multi-parameter discrimination. However, the unprecedented spatial parallelism provided by SDM introduces significant challenges, including inter-channel crosstalk, complex signal demultiplexing, and massive data volumes. This paper systematically explores how AI, particularly machine learning (ML) and deep learning (DL), is being leveraged not merely as a tool but as an indispensable core technology to mitigate these impairments. We critically analyze AI’s role in digital crosstalk suppression, intelligent mode demultiplexing, signal denoising, and solving complex inverse problems for parameter estimation. Furthermore, we highlight how this AI–SDM synergy enables capabilities beyond the reach of either technology alone, such as super-resolution sensing and predictive analytics. The discussion is extended to include the critical supporting pillars of this ecosystem, such as advanced interrogation techniques and the associated data management challenges. Finally, we provide a forward-looking perspective on the trajectory of the field, outlining a path toward cognitive sensing networks that are self-calibrating, adaptive, and capable of autonomous decision-making. This review is intended to serve as a foundational reference for researchers and engineers at the intersection of photonics and intelligent systems, illuminating the pathway toward tomorrow’s intelligent sensing infrastructure. Full article

Objective: To evaluate the utility of synovial iron quantification using Magnetic resonance imaging (MRI) in assessing structural joint damage in the knee of patients with rheumatoid arthritis (RA). Methods: This cross-sectional study employed a two-stage design. In the initial comparative stage, 6 patients with RA and 5 patients with osteoarthritis (OA) were recruited to compare synovial R2* values, a metric derived from iterative decomposition of water and fat with echo asymmetry and least-squares estimation quantitation (IDEAL-IQ) MRI sequences representing synovial iron content. Following this, the RA cohort was expanded to a total of 51 patients to investigate the association between R2* values and clinical parameters, including disease activity and bone erosion. Synovial fluid iron levels were measured with an Iron Assay Kit and synovial iron deposits were semi-quantified via Prussian blue staining. Associations between R2* and clinical and laboratory parameters, including inflammatory factors and joint damage indices, were analyzed using Spearman’s rank correlation. Univariate and multivariate ordered logistic regression models were employed to identify factors associated with bone erosion severity. An R2*-based nomogram was developed and validated using receiver operating characteristic (ROC) analysis and calibration curves. Results: Synovial R2* values were significantly higher in RA patients than those with osteoarthritis (53.66 S⁻¹ vs. 31.38 S⁻¹, p < 0.05), consistent with Prussian blue staining results. While synovial R2* values showed no significant correlation with systemic iron metabolic markers, inflammatory indicators, or the Disease Activity Score 28, they were positively correlated with bone erosion severity (ρ = 0.500, p < 0.001) and negatively associated with the joint space width (ρ = −0.307, p < 0.05). Multivariate analysis identified R2* as an independent indicator linked to bone erosion extent (OR = 2358.336, p < 0.001). The R2*-based nomogram demonstrated good discriminative performance. (AUC = 0.83). Conclusions: The R2* value derived from IDEAL-IQ MRI is a reliable tool for quantifying synovial iron and may represent a promising non-invasive imaging biomarker reflecting bone erosion in RA patients. Full article

This work presents a portable polarimetric multispectral imaging (PMSI) system operating in the visible to shortwave infrared range (VNIR–SWIR: 400–1700 nm) and its application to target detection, discrimination from aquatic backgrounds, and polymer identification. The instrument integrates two synchronized cameras with motorized bandpass filters and piezoelectric polarization control, enabling the acquisition of 48 wavelength–polarization measurements per capture. This configuration allows the extraction of both intensity-based and polarimetric features, including the degree of linear polarization (DoLP). A complete radiometric and polarimetric calibration framework is implemented, encompassing system response characterization, polarization-dependent gain correction, and reflectance normalization under variable illumination. Experiments conducted on a representative set of 16 polymer materials show that polarimetric information consistently improves class separability compared to intensity-only features, with a mean gain of 6.9 (95% CI: 6.35–8.47). Although the correlation between intensity- and DoLP-based separability is moderate (r = 0.44), the results indicate complementary identification capability. Material recoverability was further evaluated using spectral unmixing techniques (VCA, N-FINDR, and PPI), with VCA offering the best accuracy–complexity trade-off on the calibrated Stokes reflectance dataset. Despite these gains, identification among chemically similar polyethylene variants remains challenging due to limited spectral and polarimetric contrast. An underwater detectability study under natural illumination reveals strong wavelength-dependent constraints: SWIR penetration is limited to 4 cm, whereas VNIR bands (430–550 nm) preserve detectability up to 20 cm, with DoLP enhancing edge visibility. These results motivate future validation in more complex aquatic conditions and with increased spectral dimensionality. Full article

Trace quantifications of arsenic (As) in foods by energy-dispersive X-ray fluorescence (ED-XRF) spectrometry are hindered by spectral overlap from lead (Pb) at characteristic emission lines. This study employed artificial neural networks (ANN) to statistically model and correct for As/Pb spectral overlap, enabling accurate As quantifications in rice-based foods. Calibration standards were prepared by pelletizing milled rice spiked with As and Pb, and validation was performed using a certified reference material, commercial rice-based foods, and Pb-spiked commercial foods. As calibration metrics were great (R² = 0.92, standard error in calibration = 41.20 µg kg⁻¹). The validation assessment achieved acceptable error using the As reference material (−19.43% error) and in commercial rice-based foods containing low Pb content (6 of 11 As determinations in agreement with the reference method). Additionally, accurate predictions of As were found in the presence of significant Pb interference (absolute mean error = 14.11% in Pb-spiked commercial foods). Overall, ANN modeling for Pb exhibited poor performance during both calibration and validation. This work demonstrates the usability of an ANN to address the As/Pb overlapping issue while offering insights into the strengths and weaknesses of ANNs when coupled with ED-XRF for trace elemental quantifications in foods. Full article

Cardiovascular-kidney-metabolic (CKM) syndrome integrates excess adiposity, metabolic dysfunction, kidney impairment, subclinical cardiovascular diseases, and clinical events along a staged continuum that invites unified prevention and treatment. Ultra-processed foods (UPFs) are a complex, high-prevalence exposure that may influence risk across CKM stages through nutrient profiles, additives, processing-induced compounds, and packaging-related contaminants. This review synthesizes epidemiologic, mechanistic, and translational evidence with attention to exposure definition and analytic rigor. We summarize NOVA-based UPF operationalization across dietary assessment tools, highlighting misclassification of mixed dishes, brand heterogeneity, and energy under-reporting, and we propose further examination of energy-adjusted models, calibration, and harmonized metrics. Observational studies consistently associate higher UPF intake with adiposity, diabetes, chronic kidney disease, cardiovascular events, and mortality, with modest to moderate effect sizes that are heterogeneous across populations. Mechanistic data from metabolomics, lipidomics, proteomics, and the gut microbiome converge on pathways of inflammation, lipid metabolism, oxidative and metabolic stress, and intestinal barrier dysfunction; in selected cohorts, multi-omics modules account for a substantial minority of UPF-outcome associations. We outline quality-control pipelines, batch-effect prevention/correction, and multiple-testing control necessary for reproducible diet-omics. Translationally, targeted lipidomic and proteomic panels show promise for CKM risk stratification and monitoring but require validation, clinical thresholds, and guideline endorsement. Equity and global context, including differences in product mix, food systems, and care capacity, modify population impact. We conclude with a research agenda prioritizing harmonized exposure metrics, error-aware modeling, standardized multi-omics workflows, and adequately powered, stage-specific interventions capable of testing mediation and prognostic utility. Full article