Search Results (5,046)

In the context of retrofitting existing buildings into nearly zero-energy buildings (NZEBs), in situ assessment methods have proven reliable for evaluating the performance of building components, including glazing systems. However, these methods are often time-consuming, intrusive to occupants, and disruptive to building operations. This study investigates the potential of a machine learning approach—multiple linear regression (MLR)—to predict the dynamic performance of an office building’s glazing system by analyzing surface temperature variations and their impact on nearby thermal comfort. The models were trained using in situ data collected over just two weeks—one in September and one in December—but were applied to predict the glazing performance on multiple other dates with diverse weather conditions. Results show that MLR predictions closely matched nighttime measurements, while some discrepancies occurred during the daytime. Nevertheless, the machine learning model achieved a daytime prediction accuracy of approximately 1.5 °C in terms of root mean square error (RMSE), which is lower than the values reported in previous studies. For thermal comfort evaluation, the MLR model identified the periods with thermal discomfort with an overall accuracy of approximately 92%. However, during periods when the difference between predicted and measured operative temperatures exceeded 1 °C, the thermal comfort predictions showed greater deviation from actual measurements. The study concludes by acknowledging its limitations and recommending a future approach that integrates machine learning with laboratory-based techniques (e.g., hot-box setups and solar simulators) and in situ measurements, together with a broader variety of glazing samples, to more effectively evaluate and enhance prediction accuracy, robustness, and generalizability. Full article

Background/Objectives: In Spain, a high proportion of children do not meet the recommended daily levels of physical activity (PA), which highlights the urgent need to understand the motivational factors that could influence PA behavior. Self-Determination Theory is a widely used approach for assessing motivation toward exercise, employing instruments such as the Behavioral Regulation in Exercise Questionnaire (BREQ-3). However, despite the cognitive and linguistic differences that limit its direct application, this tool has not yet been adapted for children aged 6–12 years. This study aimed to adapt the BREQ-3 for use with Spanish schoolchildren and to evaluate its validity and reliability in this age group. Methods: The BREQ-3 for children (BREQ-3C) was linguistically and culturally adapted. Comprehension was tested through cognitive interviews, and reliability was assessed via a test–retest with 125 Spanish schoolchildren. Statistical analyses: Confirmatory factor analysis (CFA), Cronbach’s alpha, and the intraclass correlation coefficient (ICC) were used to evaluate validity and reliability. Results: CFA supported the factorial structure of the adapted BREQ-3 for primary schoolchildren, showing acceptable model fit indices (chi-square minimum discrepancy/degrees of freedom (CMIN/df) = 1.552, root mean square error of approximation (RMSEA) = 0.053, comparative fit index (CFI) = 0.891, Tucker-Lewis index (TLI) = 0.870). Internal consistency ranged from poor to excellent for all items and the total score of the questionnaire (Cronbach’s alpha (α): 0.535 to 0.911), except for items 3, 13, 20, and 21, where the internal consistency was unacceptable. Test–retest reliability was generally satisfactory, with ICC values indicating fair to excellent temporal stability (ICC: 0.248 to 0.911). The measurement error indicators (standard error of measurement percentage (SEM%) and minimal detectable change percentage (MDC%)) varied widely, particularly for the less reliable items. Most item scores were not significantly different between the test and retest groups, although items 2, 3, 5, 9, 17, 19, and 20 were significantly different. Conclusions: The BREQ-3C has promising psychometric properties for assessing exercise motivation in children aged 6–12 years. This tool shows potential for use in research, education, and health interventions to understand and promote physical activity motivation in primary schools. Full article

The anti-slip performance of road surfaces directly affects traffic safety, yet existing evaluation methods based on texture features often suffer from limited interpretability and low accuracy. To overcome these limitations, a portable 3D laser surface analyzer was used to acquire road texture data, while a dynamic friction coefficient tester provided friction measurements. A multi-view fractal dimension index was developed to comprehensively describe the complexity of texture across spatial, cross-sectional, and depth dimensions. Combined with road surface temperature, this index was integrated into an XGBoost-based prediction model to evaluate friction at driving speeds of 10 km/h and 70 km/h. Comparative analysis with linear regression, decision tree, support vector machine, random forest, and backpropagation (BP) neural network models confirmed the superior predictive performance of the proposed approach. The model achieved backpropagation (R²) values of 0.80 and 0.82, with root mean square errors (RMSEs) of 0.05 and 0.04, respectively. Feature importance analysis indicated that fractal characteristics from multiple texture perspectives, together with temperature, significantly influence anti-slip performance. The results demonstrate the feasibility of using non-contact texture-based methods to replace traditional contact-based friction testing. Compared with traditional statistical indices and alternative machine learning algorithms, the proposed model achieved improvements in R² (up to 0.82) and reduced RMSE (as low as 0.04). This study provides a robust indicator system and predictive model to advance road surface safety assessment technologies. Full article

Soil thermal conductivity (λ) is a critical parameter governing heat transfer in geothermal exploitation, nuclear waste disposal, and landfill engineering. This study explores the thermal conductivity characteristics of silty clay and develops a prediction model using the actively heated fiber-optic method based on fiber Bragg grating technology. Tests analyze the effects of particle content (silt and sand), dry density, moisture content, organic matter (sodium humate and potassium humate), and salt content on λ. Results show λ decreases with increasing silt, sand, and organic matter content, while it increases exponentially with dry density. The critical moisture content is 50%, beyond which λ declines, and λ first rises then falls with salt content exceeding 2%. Sensitivity analysis reveals dry density is the most influential factor, followed by sodium humate and silt content. A modified Johansen model, incorporating shape factors correlated with influencing variables, improves prediction accuracy. The root mean squared error decreases to 0.087, and coefficient of determination increases to 0.866. The study provides an accurate method for measuring thermal conductivity and enhances understanding of the heat-transfer mechanism in silty clay. Full article

Background: Bulk RNA-seq is a cost-effective method for measuring average gene expression in tissue samples, but its lack of single-cell resolution limits the understanding of cellular heterogeneity. Computational deconvolution aims to infer cell-type proportions from bulk RNA-seq data; however, the accuracy of existing methods needs improvement, especially in complex tissues. Methods: In this study, we introduce DiffFormer, a novel deconvolution model that, for the first time, integrates a conditional diffusion model with a Transformer architecture. We systematically evaluated DiffFormer on four pseudo-bulk datasets and validated it on a gold-standard real-world dataset with FACS-based ground truth. Results: DiffFormer demonstrated consistent and strong performance across all test datasets, outperforming existing methods and a baseline MLP-based diffusion model (DiffMLP). For instance, on the pbmc3k dataset, DiffFormer reduced the Root Mean Square Error (RMSE) from 0.1060 to 0.0120 compared to DiffMLP. This advantage was further confirmed on the real-world dataset, where DiffFormer achieved the highest Pearson Correlation Coefficient (PCC). Conclusions: This work provides a high-precision, reproducible tool for cellular deconvolution. Crucially, the direct comparison with an MLP-based diffusion model provides definitive evidence that the Transformer architecture is key to its success, highlighting the potential of such models for solving complex bioinformatics problems. Full article

Despite its potential danger, site amplification effects are often neglected in seismic hazard analysis. Appropriate amplification factors can be determined from shear wave velocity, but impracticality in in situ measurements leads to reliance on regional correlation with geotechnical parameters such as SPT N-value. Modified power law and logarithmic equations were derived from past correlation studies to determine V_s30 values for each borehole location in the City of Manila. V_s30 profiles were spatially interpolated using the inverse-distance weighted and thin-spline methods to approximate the variation in shear wave velocities and add more detail to the existing contour map for soil profile classification across Metro Manila. Statistical analysis of the interpolated models indicates percentage differences ranging from 0 to 10% with a normalized root mean square error of nearly 5%. Generated equations and geospatial models in the study may be used as a basis for a seismic microzonation model for Metro Manila, considering other geological and geophysical layers. Full article

A daily-scale flux variance (FV) method, which employs low-frequency air temperature measurements, was assessed against eddy covariance (EC) measurements of sensible and latent heat fluxes at four sites representing grassland and cropland ecosystems. The sensible heat flux was estimated using two daily-scale FV approaches: M1 (separating daytime and nighttime data) and M2 (integrating daily data), both derived from conventional formulations. The latent heat flux was extracted as a residual of the energy balance closure with the FV-estimated sensible heat flux and additional measurements of net radiation and soil heat flux. The results showed that the FV method performed poorly in estimating sensible heat flux across all four sites, primarily due to the negative flux values from cropland sites. In contrast, latent heat flux estimation showed reasonable agreement with EC measurements. Notably, upscaling the FV method from a half-daily (M1) to a daily (M2) scale did not improve the accuracy of sensible and latent heat flux estimations for most sites. The best performance for latent heat flux was achieved with M1 at a cropland site (YF), yielding a slope of 0.98, determination coefficient of 0.88, and root mean square error of 13.13 W m⁻². Overall, the daily-scale FV method—requiring only low-frequency air temperature data from microclimate systems—offers a promising approach for evapotranspiration monitoring, particularly at basic meteorological stations lacking high-frequency instrumentations. Full article

Background/Objectives: The present case–control study aims to compare the symmetry of the sella turcica and cranial base of nine patients with anterior unicoronal synostotic plagiocephaly (ASP) and nine healthy patients referred to the maxillofacial unit of the Fondazione Policlinico Universitario Agostino Gemelli. The primary aim of this study is to assess changes in the morphology of the sella turcica and skull base in comparison with a healthy control population using both a 2D and 3D analysis of the sella turcica and skull base. Methods: Computed tomography (CT) scans of nine ASP patients from the Fondazione Policlinico Universitario Agostino Gemelli in Rome were retrieved. A quantitative evaluation of the skull base and the sella turcica was performed through the asymmetry index (A.I.), obtained from the comparison of the point-to-point distances ipsilateral and contralateral to the synostosis. A qualitative three-dimensional (3D) evaluation of the asymmetry of the sella turcica was performed by comparing each sella model with its mirrored counterpart; then, the root mean square (RMS) displacement between the original and mirrored 3D models was calculated. Results: The results showed higher A.I. values in the study group, particularly the length of the anterior cranial fossa, with A.I. values of 7.96 (study) vs. 0.02 (control). Conclusions: The higher values of the asymmetry index observed in the study group supported the presence of statistically significant asymmetries in the sella and cranial fossa measurements compared to the control group. Full article

Milepost-assisted positioning estimates the distance between a vehicle-mounted camera and a milepost as a reference position for autonomous driving. However, the accuracy of monocular metric depth estimation is compromised by camera installation angle, milepost inclination, and image occlusions. To solve the problems, this paper proposes a two-stage monocular metric depth estimation with boundary calibration and geometric optimization. In the first stage, the method detects a milepost in one frame of a video and computes a metric depth map of the milepost region by a monocular depth estimation model. In the second stage, in order to mitigate the effects of road surface undulation and occlusion, we propose geometric optimization with road plane fitting and a multi-frame fusion strategy. An experiment using pairwise images and depth measurement demonstrates that the proposed method exceeds other state-of-the-art methods with an absolute relative error of 0.055 and root mean square error of 3.421. Full article

This study investigates the effects of different tungsten (W) doping contents on the optical transmittance, surface roughness, residual stress, and microstructure of evaporated vanadium dioxide (VO₂) thin films. W-doped VO₂ thin films with varying tungsten concentrations were fabricated using electron beam evaporation combined with ion-assisted deposition techniques, and deposited on silicon wafers and glass substrates. The optical transmittances of undoped and W-doped VO₂ thin films were measured by UV/VIS/NIR spectroscopy and Fourier transform infrared (FTIR) spectroscopy. The root mean square surface roughness was measured using a Linnik microscopic interferometer. The residual stress in various W-doped VO₂ films was evaluated using a modified Twyman–Green interferometer. The surface morphological and structural characterization of the W-doped VO₂ thin films were performed by field-emission scanning electron microscopy (FE-SEM) and X-ray diffraction (XRD). Raman spectroscopy was used to analyze the structure and vibrational modes of different W-doped VO₂ thin films. These results show that the addition of tungsten significantly alters the structural, optical, and mechanical properties of VO₂ thin films. Full article

The backscattering coefficient of aquatic particles ($b_{bp}\left(\lambda \right)$) is one of the most important inherent optical properties in remote sensing. Due to the practical difficulties associated with measurements of the volume scattering function (VSF) over the whole backward hemisphere (90°–180°), $b_{bp}\left(\lambda \right)$ is estimated using either a single-angle approach, which employs the VSF at a fixed angle multiplied by a conversion factor ${\chi }_p(\theta ;\lambda )$, or a multi-angle approach, which uses the VSF at multiple angles with polynomial fitting. The angular variation in the VSF in the backward angles introduces uncertainties into $b_{bp}\left(\lambda \right)$ estimation. In this study, 178 VSF datasets from the global ocean were investigated. ${\chi }_p$ exhibited wavelength, regional, and angular variations. Although ${\chi }_p$ exhibited the lowest variability, at 120° (${\chi }_p(120°;\lambda )$), the single-angle approach exhibited a 12.71% mean absolute percent difference (MAPD) and a root mean squared error (RMSE) of approximately $4.02\times {10}^{-3}$${\mathrm{m}}^{-1}$. ${\chi }_p(140°;\lambda )$ exhibited larger variations at different wavelengths and in coastal regions. The three-angle approach exhibits wavelength independence and lower uncertainties, but the uncertainty of the polynomial fitting results at angles greater than 150° is relatively large, and the MAPD is still up to 10.92%. A better four-angle approach (100°, 120°, 140°, and 160°) was proposed, which could accurately determine $b_{bp}\left(\lambda \right)$ with the lowest MAPD (3.12%) and RMSE ($0.86\times {10}^{-3}$${\mathrm{m}}^{-1}$). Notably, expanding to five angles provided minimal additional improvements, with the reduction in the MAPD being less than 1% compared to that under the four-angle approach. This study provides valuable insights into developing advanced optical sensors with better angular configurations for measuring $b_{bp}\left(\lambda \right)$. Full article

In modern industrial manufacturing, the mechanical properties of large bearing housing castings are critical to equipment reliability and lifespan. Traditional prediction methods relying on experimental testing and empirical formulas face challenges such as high costs, limited samples, and inadequate generalization capabilities. This study presents a machine learning approach for predicting mechanical properties of ZG270-500 cast steel, integrating multivariate data (chemical composition, process parameters) to establish an efficient predictive model. Utilizing real-world production data from a certain foundry and forging plant, the research implemented preprocessing steps including outlier handling, data balancing, and normalization. A systematic comparison was conducted on the performance of four algorithms: Backpropagation Neural Network (BPNN), Support Vector Regression (SVR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The results indicate that under small-sample conditions, the SVR model outperforms other models, achieving a coefficient of determination (R²) between 0.85 and 0.95 on the test set for mechanical properties. The root mean square errors (RMSE) for yield strength, tensile strength, elongation, reduction in area, and impact energy are 7.59 MPa, 7.52 MPa, 0.68%, 1.47%, and 5.51 J, respectively. Experimental validation confirmed relative errors between predicted and measured values below 4%. SHAP value analysis elucidated the influence mechanisms of key process parameters (e.g., pouring speed, normalization holding time) and elemental composition on mechanical properties. This research establishes an efficient data-driven approach for large casting performance prediction and provides a theoretical foundation for guiding process optimization, thereby addressing the research gap in performance prediction for large bearing housing castings. Full article

This paper aims to assess the water quality of the Ismailia Canal, Egypt, in accordance with Article 49 of Law 92/2013. QUAL2K and Convolutional Neural Networks and Long Short-Term Memory (CNN-LSTM) are utilized to simulate the water quality parameters of dissolved oxygen (DO), pH, biological oxygen demand (BOD), chemical oxygen demand (COD), total phosphorus (TP), nitrate nitrogen (NO₃-N), and ammonium (NH₃-N) in winter and summer 2023. The parameters of the QUAL2K and CNN-LSTM models were calibrated and validated in both winter and summer through trial and error, until the simulated results agreed well with the observed data. Additionally, the model’s performance was measured using different statistical criteria such as mean absolute error (MAE), root mean square (RMS), and relative error (RE). The results showed that the simulated values were in good agreement with the observed values. The results show that all parameter concentrations follow and did not exceed the limit of Article 49 of Law 92/2013 in winter and summer, except for dissolved oxygen concentration (8.73–4.53 mg/L) in winter and summer, respectively, which exceeds the limit of 6 mg/L, and in June, biological oxygen demand exceeds the limit of 6 mg/L due to increased organic matter. It is imperative to compare QUAL2K and CNN-LSTM models because QUAL2K provides a physics-based simulation of water quality processes, whereas CNN-LSTM employs deep learning in modeling complex temporal patterns. The two models enhance prediction accuracy and credibility towards enabling enhanced decision-making for Ismailia Canal water management. This research can be part of a decision support system regarding maximizing the benefits of the Ismailia Canal. Full article

Background: Persistent fatigue is among the most commonly reported symptoms in patients suffering from post-acute sequelae of SARS-CoV-2 infection (long COVID). Autonomic dysfunction, measurable via heart rate variability, has been implicated as a contributing factor. Osteopathic manipulative treatment is a manual therapeutic approach that targets autonomic balance and may offer a novel intervention for long COVID-related fatigue. Methods: In this single-blind, randomized controlled trial, 42 participants (mean age 51 ± 11 years; fatigue severity score: 31 ± 5 points) with long COVID and persistent fatigue ≥12 weeks post-infection were allocated to either a 45 min standardized osteopathic treatment (n = 21) or a sham-treatment group (n = 21). Heart rate variability was assessed using a 10 min resting electrocardiogram before intervention, immediately after, and again 48 h later. The analysis of heart rate variability encompassed time-domain indices, including the root mean square of successive differences, the standard deviation of normal-to-normal intervals, mean heart rate, and mean RR interval. Additionally, frequency-domain measures such as low-frequency, high-frequency, total power, and the LF/HF ratio were considered. Results: The osteopathy group showed a statistically significant increase in root mean square of successive differences post-treatment (p < 0.01), accompanied by a decrease in the stress index (p < 0.05) and an increase in the mean of the standard deviations of RR intervals (p < 0.05). Significant between-group differences were observed for mean heart rate and mean of RR intervals (p < 0.05). Frequency-domain measures also improved significantly from baseline in the intervention group. Outlier patterns suggest potential subgroup effects, possibly due to underlying dysautonomia. Conclusions: A single session of osteopathic treatment significantly enhanced short-term heart rate variability in long COVID patients with fatigue. These findings highlight the potential role of manual autonomic modulation as a supportive therapy in long COVID management. Further research is needed to assess the long-term effects and optimal treatment frequency of osteopathic manipulative treatment in this population. Full article

This paper presents a novel electro-thermal modeling approach for a lithium-ion battery pack in an electric vehicle (EV), along with parameter identification using controller area network (CAN) data collected from chassis dynamometer and real-world driving tests. The proposed electro-thermal model consists of a first-order equivalent circuit model (ECM) and a lumped-parameter thermal network in considering a simplified cooling circuit layout and temperature distributions across four distinct zones within the battery pack. This model captures the nonuniform heat transfer between the pack modules and the coolant, as well as variations in coolant temperature and flow rates. Model parameters are identified directly from vehicle-level test data without relying on laboratory-level measurements. Validation results demonstrate that the model can predict terminal voltage with an RMSE of less than 6 V (normalized root mean square error of less than 2%), and battery module surface temperatures with root mean square errors of less than 2 °C for over 90% of the test cases. The proposed approach provides a cost-effective and accurate solution for predicting electro-thermal behavior of EV battery systems, making it a valuable tool for battery design and management to optimize performance and ensure the safety of EVs. Full article