Search Results (27,584)

This study presents a data-driven framework for the mechanistic interpretation of asphalt pavement responses using an integrated smart sensing and monitoring system deployed on a national highway in Thailand. A fully instrumented pavement test section was developed, incorporating a multi-sensor embedded network and a field data acquisition platform integrated with weigh-in-motion (WIM) technology. The system consists of 54 sensors, including strain gauges, pressure cells, moisture sensors, and thermocouples, installed at multiple depths to capture high-resolution stress–strain responses under controlled heavy-vehicle loading. Field measurements were analyzed and compared with classical mechanistic models, including Boussinesq’s theory, Odemark’s equivalent thickness method, and Burmister’s multilayer elastic theory. The results demonstrate good agreement for vertical stress predictions in deeper layers, while significant discrepancies were observed in strain responses, particularly in the asphalt layer, where measured tensile strains were up to 2.5 times higher than theoretical estimates. The findings indicate that conventional elastic models provide useful first-order approximations; however, discrepancies were observed in representing the viscoelastic behavior of asphalt materials under real loading conditions. Furthermore, the integration of sensor data with traffic loading information confirms that axle load magnitude is the dominant factor governing pavement responses, whereas vehicle speed primarily influences load duration. The proposed framework demonstrates the potential of smart sensing systems for enabling automated, data-driven pavement analysis and supporting digital twin-based infrastructure management. Full article

(1) Background: Posterior crossbite associated with maxillary transverse deficiency is commonly managed with maxillary expansion, yet the correspondence between laboratory activation behavior and the clinical response of nickel–titanium leaf-spring expanders remains insufficiently defined; therefore, this study aimed to compare in vitro and in vivo performance of the Leaf Expander^® and to assess their agreement. (2) Methods: A retrospective sample of 15 mixed-dentition patients (7–10 years) treated at two university centers with a Leaf Expander^® (6 mm screw; 900 g) was evaluated; interpremolar (E–E), intermolar (6–6), and intercanine (C–C) distances were recorded at baseline (T0, digital models) and at follow-up visits (T1–T5, caliper measurements), while mechanical compression testing (Instron 3365) quantified force release across the activation sequence; normality (Shapiro–Wilk), parametric analyses, and Pearson correlation were used. (3) Results Posterior crossbite correction was achieved in all completed cases, with mean total increases (T0–T5) of 5.4 mm (E–E), 4.4 mm (6–6), and 6.0 mm (C–C); early expansion (T1–T0) averaged 2.5 mm at E–E, and laboratory curves showed an activation peak followed by sustained force release (~6.5–9 N) and a residual-load phase. Agreement between declared activation and clinical response was higher for E–E and 6–6 than for C–C, which showed greater variability. (4) Conclusions: These findings support the Leaf Expander^® as an effective compliance-free slow expansion device and indicate that laboratory force behavior can help interpret the clinical expansion timeline, including delayed expression after activation. Full article

Equine swimming is increasingly used for injury prevention and rehabilitation, but objective analysis of movement during swimming remains limited compared to land-based locomotion. Spatiotemporal parameters are essential for evaluating therapeutic outcomes, yet capturing these parameters is technically challenging due to difficulties in observing limb motion in water. Inertial sensors, already widely applied in equine science, offer a promising solution for measuring swimming kinematics objectively. The objective of this study was to evaluate the reliability of inertial sensors placed on equine distal limbs in detecting key spatiotemporal events during swimming by comparing it with video-based detection made by veterinarians. For the duration of the hindlimb swimming cycle, 24 data values were analysed and showed an “excellent” agreement, with an intraclass correlation coefficient = 0.96, 95% CI: 0.904–0.983, and Bland–Altmann analysis showed an upper limit of agreement of 50 ms (95% CI: 70 ms, 30 ms) and lower one of −60 ms (95% CI: −40 ms, −80 ms). The estimates of the “swimming” duty factor of the hindlimb (n = 24) demonstrated “moderate” to “excellent” with intraclass correlation of 0.82 (95% CI: 0.625–0.920) and limits of agreement of 4.39% (95% CI: 6.21%, 2.53%) and −5.28% (95% CI: −3.42%, −7.14%). The results of the forelimb were mixed, suggesting that the cycle duration and “swimming” duty factor parameters determined for this limb should be used with caution. Overall, the findings confirm that inertial sensors, particularly on the hindlimbs, provide reliable spatiotemporal measurements and are well suited for studying equine swimming. Full article

This study aimed to estimate vertical ground reaction force (vGRF) and lower-limb joint moments during football cutting movements using a trunk-mounted inertial measurement unit (IMU) combined with a Random Forest model, and to validate the feasibility of this approach. IMU data collected during 45° cutting tasks were corrected using an Extended Kalman Filter (EKF)). The model demonstrated good and consistent performance for vGRF (coefficient of determination, R²= 0.766; correlation coefficient, r = 0.796) and sagittal plane moments of the ankle and knee (R²= 0.661–0.689, r = 0.807–0.842).While Bland–Altman analysis indicated low bias and generally good agreement, precision at the individual-trial level and accuracy for non-sagittal plane moments somewhat reflected the inherent within-player trial-to-trial variability in movement execution, particularly in non-sagittal loading patterns. It should be noted that performance estimates under the current trial-based validation design may differ from those obtained using a subject-independent framework such as leave-one-subject-out cross-validation. This study demonstrates that a single trunk-mounted IMU can reliably estimate key lower-limb loading patterns, providing a practical foundation for wearable-based kinetic monitoring in applied football settings. Full article

Hypernasality is a common characteristic of several speech disorders and can significantly affect perceived speech intelligibility and quality. Nasometry quantifies nasalance by calculating the proportion of acoustic energy emitted from the nasal cavity relative to the combined nasal and oral acoustic output during speech production and is commonly used in clinical assessment and research. However, commercially available nasometers are costly and limited in portability, restricting their use in resource-limited or remote settings. The primary purpose of this study was to design and build a low-cost, open-source mobile nasometer prototype (“mNasometer”) by leveraging advances in 3D printing, off-the-shelf electronic components, and a custom open-source mobile application. A secondary aim was to compare the electroacoustic and subjective performance of mNasometer with that of a gold-standard commercial nasometer. Electroacoustic analyses focused on comparing long-term averaged spectra and the oral/nasal acoustic isolation between the gold-standard commercial nasometer and the proposed mNasometer, which incorporates a 3D-printed nasal separation plate. In addition, nasalance scores were collected from ten healthy young adult participants using both systems during structured speech production tasks (i.e., reading standard passages or nasal sentences). Agreement between devices was evaluated using correlational analyses and comparative statistical procedures. Long-term averaged spectra exhibited similar profiles between the commercial nasometer and the mNasometer across different test stimuli, indicating comparable capture of stimulus energy distributions. Although the mNasometer demonstrated reduced oral–nasal acoustic isolation relative to the commercial system, objective nasalance scores followed similar overall trends between devices, with statistically significant stimulus-dependent differences observed. Frame-wise correlational analyses revealed significant correlations between nasalance measures obtained from the commercial nasometer and the mNasometer across most of the speech production tasks, suggesting that the reduced isolation did not critically compromise measurement correspondence. In summary, the low-cost, open-source mNasometer prototype provides nasalance measurements that show promising agreement with those of a gold-standard commercial device. Its reduced cost and increased portability suggest potential for expanded research and field-based applications in the objective assessment of nasalance. Full article

Background/Objectives: The Global Lung Initiative (GLI) 2022 race-neutral spirometry reference equations were introduced to improve interpretability across populations; however, their performance in Middle Eastern adolescents remains insufficiently validated. This study evaluated the applicability of GLI-2022 among healthy Jordanian adolescents. Methods: Healthy adolescents were recruited from secondary schools across multiple Jordanian cities (July–November 2025). Spirometry was performed according to ATS/ERS standards using a single device and standardized procedures. GLI-2022 predicted values and z-scores were derived for forced expiratory volume in one second (FEV₁), forced vital capacity (FVC), and FEV₁/FVC. Calibration was assessed using mean (SD) z-scores and the proportion below the lower limit of normal (LLN; z < −1.645). Agreement between measured and predicted values was examined using Bland–Altman methods. LLN-based pattern classifications were compared with those obtained using the local reference equation and GLI-2012. Results: A total of 921 adolescents (482 males, 439 females; mean age 15.7–16.0 years) were included. GLI-2022 produced positive mean z-scores for FEV1 (0.51–0.73) and FVC (0.51–0.69), with low proportions below LLN for both indices (<2% in each sex), indicating underestimation of predicted lung volumes. Exact binomial testing confirmed that the observed proportions below LLN for FEV1 and FVC were significantly lower than the expected 5% in both sexes (all p < 0.001). The FEV1/FVC ratio showed smaller deviations (mean z 0.07–0.19), with 4.1% of females and 5.8% of males below LLN, and these proportions did not differ significantly from 5% (female p = 0.444; male p = 0.402). Mean observed-minus-predicted biases for FEV1 were +0.185 L in females and +0.306 L in males, and for FVC were +0.224 L and +0.351 L, respectively; FEV1/FVC bias was −0.15 percentage points in females and +0.60 percentage points in males. LLN-based pattern classification showed 98.7% overall agreement with the local equation and 99.7% with GLI-2012; concordance for obstructive and possible restrictive patterns was 93.5% and 100.0%, respectively. Conclusions: In healthy Jordanian adolescents, GLI-2022 appears to underestimate predicted FEV₁ and FVC, yielding upward-shifted z-scores and fewer volume indices below LLN, while the ratio is less affected. Although LLN-based pattern classification was largely preserved, population-specific validation remains necessary before routine clinical adoption of GLI-2022 in Jordanian adolescents; extrapolation to other Middle Eastern adolescent populations should await additional regional validation. Full article

The buckling and post-buckling responses of carbon fibre-reinforced polymer (CFRP) structures are strongly affected by geometric imperfections, boundary conditions, and material nonlinearities, making their reliable numerical prediction challenging. This work presents an integrated experimental–numerical investigation of a stiffened CFRP panel subjected to compressive loading, with the aim of improving model validation in instability regimes. The experimental campaign combines full-field measurements obtained through digital image correlation with local strain data from strain gauges, adopting a back-to-back configuration to capture the strain reversal associated with global buckling. The experimental results are compared with nonlinear finite element simulations incorporating intralaminar damage based on Hashin’s failure criteria. A good agreement between the numerical and experimental results is observed in the pre-buckling and early post-buckling regimes. However, increasing discrepancies arise at higher load levels, mainly due to manufacturing imperfections and uncertainties in boundary conditions, which influence the onset and evolution of localized deformation. Statistical indicators are employed to quantitatively assess the correlation between the experimental and numerical responses. The analysis focuses on the key response parameters, including the load–displacement behaviour, out-of-plane displacements, strain evolution, and damage initiation, enabling a comprehensive comparison of experimental and numerical results. The results demonstrate the effectiveness of combining full-field and point-wise measurements for validating numerical models of composite structures. Furthermore, the study highlights the limitations of idealized modelling assumptions and provides insights into the sensitivity of CFRP structures to imperfections in post-buckling and failure regimes. Full article

Online and blended classrooms widen access but remove the in-person cues instructors use to gauge attention. Prior work typically relies on heavy, cloud-bound or multimodal models that are hard to deploy on commodity laptops, treats attention as an unordered label without calibrated probabilities, and evaluates on subject-overlapping splits with limited robustness analysis. This creates a gap in Tiny, deployable, calibration-aware methods validated under realistic protocols. We address this gap with a TinyML, vision-only pipeline that estimates four attention levels: (Very Low, low, high, Very High ) from short webcam clips under strict on-device budgets. Each clip of $T=30$ frames at $224\times 224$ is processed by a compact hybrid encoder: a CNN extracts per frame spatial features, a BiLSTM models temporal context, and a lightweight GRU refines dynamics; three parallel branches with staggered widths encourage feature diversity before fusion. We apply structured pruning of convolutional channels and recurrent units, post-training INT8 quantization, and temperature scaling for calibrated probabilities; models are exported as ONNX. On DAiSEE with subject-independent splits, the baseline attains $99.86\%$ accuracy and $0.998$ macro-F1, with strong ordinal agreement (QWK = 0.998, ordinal MAE = 0.03). The compressed model preserves reliability (macro-F1 = 0.995, QWK = 0.995), remains robust to low light, partial occlusion, and head yaw, and yields ∼4× smaller size and ∼2.3× CPU speedups. These results indicate a deployable, privacy-preserving approach to fine-grained, on-device attention analytics. Full article

Hybrid gas separation technologies combining different physicochemical mechanisms represent a promising approach for the efficient treatment of complex natural gas mixtures. In this work, a hybrid process integrating gas hydrate crystallization and membrane gas separation was investigated for the upgrading of multicomponent natural gas-containing hydrocarbons (C₁–C₄), acid gases (CO₂ and H₂S), and inert components. Polysulfone hollow-fiber membranes were fabricated, and their gas transport properties were experimentally determined using an eight-component quasi-real natural gas mixture under elevated pressure conditions. The obtained mixed-gas permeance values were used as input parameters for the development of a detailed mathematical model of a hollow-fiber membrane module implemented in the Aspen Custom Modeler. The model was applied to simulate membrane separation of both gas- and hydrate-derived streams produced by the gas hydrate crystallizer. Simulation results were analyzed in terms of hydrocarbon composition, acid gas removal efficiency, and hydrocarbon recovery as a function of the stage-cut. The modeling predictions were validated experimentally using a laboratory membrane module integrated with the gas hydrate crystallization unit. Good agreement between the experimental data and simulation results was observed for all major components. The deviation between modeled and experimental concentrations remained small, while the discrepancy in hydrocarbon recovery was higher and reached approximately 10–20%, which is attributed to the cumulative uncertainty of flow rate and composition measurements. These results confirm the adequacy of the developed model. The hybrid process demonstrates strong complementarity between the thermodynamic selectivity of hydrate formation and the transport selectivity of membrane separation, enabling efficient removal of acid gases while maintaining acceptable hydrocarbon recovery. The results indicate that the proposed gas hydrate–membrane hybrid process is a promising strategy for advanced natural gas purification and upgrading. Full article

Background/Objectives: Histological assessment of the response to neoadjuvant immunotherapy in stage IIIB/C cutaneous melanoma is commonly performed using the INMC recommendations, which advocate embedding the entire tumour bed. However, this approach is highly resource-intensive. The original Swedish National Clinical Cancer Care Guidelines for Melanoma recommend a reduced embedding strategy, but its diagnostic performance in comparison with full embedding has not been systematically evaluated. We aimed to assess whether a reduced sampling approach based on selecting representative tumour bed slices provides comparable response classification to comprehensive embedding according to the INMC protocol. Methods: Ten consecutive patients with stage IIIB/C melanoma treated with neoadjuvant immunotherapy and subsequent lymph node dissection were included. All lymph node material was completely embedded, and the pathological response was evaluated using INMC criteria. Response classification based on the full embedding of the tumour bed was compared with reduced-sampling approaches that simulate the Swedish National Clinical Cancer Care Guidelines for Melanoma. Agreement between sampling methods was analyzed. Results: Targeted reduced sampling of two slices per lymph node demonstrated complete agreement with the full embedding for the INMC response category. Conclusions: In our study, a targeted reduced-embedding strategy focusing on slices with the largest area of tumour bed enables accurate histological assessment of response to neoadjuvant immunotherapy in stage IIIB/C melanoma while substantially reducing workload. These findings support the feasibility of targeted reduced embedding of instructions in the Swedish National Clinical Cancer Care Guidelines for Melanoma as an efficient alternative to full embedding of the lymphadenectomy specimens. Full article

Binder jetting relies on the infiltration of binder droplets into a porous powder bed, where the spatial arrangement of droplets critically influences feature formation and structural integrity. In particular, the role of droplet spacing in regulating infiltration behavior remains insufficiently understood. In this study, droplet infiltration is investigated using a reconstructed three-dimensional powder bed combined with a Volume of Fluid (VOF) model. Both single- and dual-droplet configurations are examined to isolate the effect of droplet spacing on spreading, merging, and capillary-driven penetration. The results show that droplet spacing governs the redistribution of liquid flow between lateral spreading and vertical infiltration. Three distinct regimes are identified as spacing decreases: independent infiltration at large spacing, cooperative merging at intermediate spacing, and over-penetration at small spacing. These regimes reflect a transition from isolated droplet behavior to strongly coupled infiltration within the pore network. An optimal spacing of approximately 150 μm is found to balance spreading and penetration, enabling continuous deposition with controlled infiltration depth. Experimental measurements show good agreement with numerical predictions, with an average deviation of 8.66%. The present study clarifies the mechanism by which droplet spacing controls infiltration behavior and provides practical guidance for parameter selection in binder jetting processes. Full article

Effective management of reservoir water for irrigation is crucial in arid regions prone to drought and water shortages. However, evaporation losses from reservoirs remain poorly understood. Direct measurements typically quantify evaporation only at the measurement site rather than across the entire reservoir. This study introduces the Turbulent Exchange Approach for Reservoir Evaporation Estimation (TEAREE). The TEAREE is a simple model that integrates a bulk aerodynamic formulation with Landsat 8–9 satellite water-surface temperature data and meteorological observations to estimate spatially distributed daily reservoir evaporation. The TEAREE model was first evaluated at Elephant Butte and Caballo reservoirs in NM, USA, and subsequently applied across multiple reservoirs with diverse climatic conditions to demonstrate its applicability for estimating open-water evaporation. Daily evaporation was obtained by upscaling satellite overpass-time evaporation estimates using the daily-to-instantaneous vapor pressure deficit ratio (k_e) and wind speed. The model performed strongly across 12 lakes (R² = 0.91–0.99; RMSE = 0.27–0.85 mm/day) compared with the bulk aerodynamic (B_AER) method. Comparison with eddy covariance (EC) evaporation also showed good agreement. Monte Carlo analysis indicated moderate uncertainty associated with k_e variability, supporting the operational use of a constant k_e = 0.95 for daily upscaling. Full article

Fiber-reinforced polymer (FRP) composites are widely used to strengthen reinforced concrete (RC) columns due to their high strength, durability, and ease of installation. Accurate prediction of the axial capacity of CFRP-strengthened concrete columns is essential for reliable structural design. Yet conventional empirical models often exhibit limited accuracy due to the complex interactions among structural parameters. This study develops a deep learning-based model to predict the axial capacity of CFRP-wrapped RC columns using a database of 469 experimental tests collected from published studies. A deep neural network (DNN) was optimized using the Optuna hyperparameter tuning framework and k-fold cross-validation to enhance model accuracy and robustness. Model performance was evaluated using statistical indicators, including R², RMSE, MAE, MAPE, and the a20-index. The results show excellent predictive performance with R² values approaching 0.99 and an a20-index of 0.98, demonstrating strong agreement between predicted and experimental results. Comparisons with the ACI 440.2R-17 and CSA S806-12 design codes indicate that the proposed DNN model provides significantly improved prediction accuracy, with lower errors. The developed approach offers a reliable and efficient tool for estimating the axial capacity of CFRP-strengthened concrete columns. Full article

Knowledge of the heterogeneous composition of mineral substances and technogenic materials is vital in the development of technological designs and in environmental impact assessments. However, the simultaneous application of analytical methods often reveals discrepancies between different methods that cannot be explained by the commonly used scalar agreement metrics. The current study suggests the possibility of evaluating inter-methodical agreement in the compositional data of heterogeneous substances using the example of lead smelting slag compositional data. The compositional data of 96 samples of lead smelting slag were analyzed using independent methods of XRF, ICP-OES and AAS. The agreement between the obtained results was evaluated at three levels of hierarchy: element-wise bias and dispersion, structured variability between methods and preservation of covariance structure; PCA was used as the tool for the evaluation. High agreement between the ICP-OES and AAS methods was found for the transition metals (r ≈ 0.97–0.99), with negligible bias. The presence of increased dispersion and deviations in the inter-methodical agreement was found in the case of the methods compared with the XRF method, especially for the matrix components such as Si (r ≈ 0.92). Coefficients of variation for metallic elements stay between 12 and 15%, but XRF has shown consistently higher variability for certain elements. PCA results show that despite local differences, the main covariance structure was kept across methods, with the first two components explaining about 40–45% of total variance. These results clearly indicate that high correlation does not necessarily ensure methodological interchangeability. The hierarchical framework proposed here will provide a reproducible basis for cross-method validation and support reliable data integration in complex mineral systems. Full article

The Earth–Air Heat Exchanger (EAHE), also referred to as an air–soil heat exchanger, represents an effective passive cooling technology that exploits the thermal inertia of the ground. This study presents a combined experimental and analytical investigation of an EAHE system installed at the Faculty of Sciences of Agadir (Morocco). A steady-state analytical model based on convective heat transfer between the airflow within a buried duct and the surrounding soil is developed to describe the axial evolution of air temperature along the exchanger. The model is formulated under a sensible heat transfer framework, where the influence of humidity is accounted for through its effect on the thermophysical properties of moist air, while latent heat transfer and condensation phenomena are neglected. An instrumented experimental setup was implemented to perform continuous measurements of air temperature and relative humidity over a seven-month monitoring period. The experimental results indicate that the outlet air temperature remains stabilized within the range of 23.5–23.8 °C, despite significant variations in ambient temperature (13–38 °C). A parametric analysis is conducted to assess the influence of duct diameter, airflow velocity, and humidity through its effect on moist air properties on the thermal performance of the system. The close agreement between experimental observations and analytical predictions demonstrates the validity and predictive capability of the proposed model. These findings highlight the potential of EAHE systems as an effective passive cooling solution for greenhouse applications in semi-arid climatic conditions. Full article