Search Results (3,431)

Conductive heat flux (G) at pavement surfaces plays a vital role in managing internal temperature variations. G can be calculated either as the residual of solar absorption, heat convection, and long-wave radiation, or as the product of thermal conductivity and the temperature gradient near the surface. Both methods, however, are subject to uncertainties due to measurement parameters. For the two methods, this study formulates the uncertainty of the conductive heat flux at the pavement surface. The experiment was designed to measure pavement interior temperatures and external weather data so that the uncertainties of the two methods can be quantified and compared. It was found that ∆G estimated by the residual method is significantly higher than that calculated using conductivity and temperature gradient. The key factors influencing ∆G in the residual method, in order, are wind speed, incident solar radiation, and reflectivity, with other factors such as surface and air temperatures, relative humidity, and emissivity having minimal impact. In contrast, the primary contributors to ∆G in the conductivity and temperature gradient method are the temperature gradient and thermal conductivity. The residual method is crucial for predicting pavement temperatures when no pre-installed temperature sensors are available, and enhancing wind speed measurement precision can significantly reduce the uncertainty of G. The study finds that the approach of estimating G through conductivity and temperature gradient showed lower uncertainty than the residual method, particularly in complex urban environments. Full article

The MASW (Multichannel Analysis of Surface Waves) method oriented toward seismic microzoning has been evolving consistently and steadily for several decades, providing increasingly reliable solutions that are consistent with field and laboratory data typical of classical geotechnics. This study evaluates the improvement achieved when using a sequence of inversion algorithms on MASW test results: first with a global algorithm—specifically Differential Evolution (DE)—and subsequently, using the best model obtained from the global search, a second local algorithm—Trust Region Reflective (TRF). This second stage refines the previous model, further adjusting it to the borehole model used as the starting point of the sequence. The procedure has been automated using a Python script that incorporates two innovations compared to traditional inversion approaches. These consist of parameterising two variables: (i) an adaptive expansion factor for the $V_s$ limits establisheda priori in the borehole model, and (ii) a subdivision into thinner layers for borehole models with excessively thick strata. This provides the algorithms with greater flexibility, particularly in scenarios with complex stratification. Additionally, to better define the deeper layers, the passive ESAC method in an “L-shape” configuration was also employed. The parameterised sequential hybrid inversion process was validated using synthetic data from two curves (Curve #1 and Curve #2), obtained by adding 5% Gaussian noise to the forward modelling results of the same initial synthetic model. The TRF refinement stage in the sequential hybrid inversion succeeded in reducing the error obtained by the global algorithm by percentages ranging from 59.7% to 5.8% across all conducted tests, confirming the stability of the methodology used. Full article

Botulinum toxin type A (BoNT-A) is an established treatment for cervical dystonia (CD), but objective neurophysiological markers across the injection cycle and between preparations are not well defined. We assessed afferent conduction (captured by CSP parameters), efferent conduction (captured by F-wave parameters) and clinical severity (TWSTRS) in 28 patients with caput-type cervical dystonia during waning (>14 weeks post-injection) and peak (4–6 weeks) phases, comparing onabotulinumtoxinA (ONA) and abobotulinumtoxinA (ABO). F-wave measures changed only modestly: F–M latency increased with ONA, while F-wave persistence decreased with ABO. In contrast, CSP measures consistently increased at peak in both groups (CSP end latency and CSP duration; both p ≤ 0.001). Overall, BoNT-A treatment phase is better reflected by CSP-derived inhibitory measures than by F-wave indices. TWSTRS improved at peak for both toxins, with no difference between ONA and ABO in clinical change (ΔTWSTRS p = 0.5514). Full article

Local impedance variations in structural waveguides partially reflect and absorb incident
flexural waves, motivating wave-based strategies for passive vibration control. This study
develops and experimentally validates a wave-energy framework to quantify and optimize
flexural wave absorption by Kelvin–Voigt attachments on a finite Timoshenko beam.
A finite element model is validated against Scanning Laser Doppler Vibrometry measurements
from a clamped–clamped aluminum beam with a passive damper mounted near
one end, with dashpot parameters identified through two independent approaches and
the discrepancies attributed to parameter uncertainty. Wave decomposition of the simulated
and measured velocity fields yields the power reflection coefficient ρ(ω) and power
absorption coefficient α(ω) over the 0–15.3 kHz band. The spring stiffness and damping
coefficient exhibit frequency-dependent optima and act as complementary, jointly tuned design
variables. Expressing dashpot location in wavelength-normalized coordinates reveals
a recurring spatial pattern in which absorption minima cluster around half-wavelength
multiples, while multiple spanwise positions yield near-peak absorption at any given
frequency. This pattern is governed primarily by the flexural wavelength, decoupling
placement from parameter tuning, and persists across clamped–clamped, clamped–free,
and free–free boundary conditions. Two independently tuned dampers further broaden the
effective absorption band by suppressing local minima in α(ω). These results demonstrate
that measurement-driven wave decomposition provides compact, physically grounded
guidelines for passive damper placement in beam structures. Full article

Acoustic propagation in stratified shallow seas is governed by finite-depth waveguiding, impedance contrasts at the seawater–seabed interface, and coupled space–time wave dynamics. Conventional numerical solvers are accurate but often require detailed environmental priors, mesh generation, and explicit time marching, increasing the cost of simulations involving complex boundaries or repeated evaluations. This study proposes a physics-informed residual network (ResNet-PINN) for continuous simulation of two-dimensional acoustic fields in shallow-sea stratified media. The framework embeds a variable-density, variable-sound-speed acoustic pressure wave equation, initial and boundary constraints, and interface-focused collocation into network training. A Gaussian initial wave packet and temporal gating are incorporated through the output transformation to improve early-time physical consistency. The model is validated against SPECFEM2D simulations and a stratified semi-analytical modal benchmark. The results show that it captures source-region spreading, main wavefront evolution, and transmission–reflection structures near the seawater–seabed interface at an equivalent frequency of approximately 477 Hz. Supplementary tests with sloping and arched interfaces and modified boundary conditions indicate adaptability to smooth interface variations. Overall, the framework provides a physically consistent neural network strategy for continuous shallow-sea acoustic field simulation and a complementary basis for future extensions to higher-frequency propagation, more complex environments, and dynamically varying ocean conditions. Full article

Intercellular communication is crucial for the coordination of skeletal muscle development. However, the intricate signaling networks that regulate chicken myogenesis are not yet fully elucidated. In this study, we utilized CellChat analysis on single-cell and single-nucleus RNA sequencing data to systematically delineate cell–cell communication patterns across five critical developmental stages of chicken skeletal muscle: embryonic day 4 (E4), day 6 (E6), day 12 (E12), day 18 (E18), and post-hatch day 30 (P30). Our findings indicate that communication architectures are highly stage-specific, with mesenchymal cells acting as the predominant signaling hub during the early embryonic stages (E4–E6), whereas fibro-adipogenic progenitors become the principal communicators during mid-to-late embryogenesis (E12–E18). At E4, the communication network was relatively simple, comprising 51 ligand–receptor pairs primarily involving the neural cell adhesion molecule, slit guidance ligand, and midkine (MK) signaling pathways between myogenic progenitors and mesenchymal cells. By E6, the network had expanded significantly, encompassing 6237 ligand–receptor pairs across 51 signaling pathways, which coincided with the emergence of multiple myogenic lineages. Peak communication complexity was observed at E12, characterized by 11,675 ligand–receptor pairs and 61 signaling pathways, reflecting the secondary wave of myogenesis. Comparative analysis across developmental stages revealed key signaling transitions: the pleiotrophin and MK pathways were predominantly active during the early phase of myogenic commitment (E4–E6), whereas the collagen, laminin, and adhesion G protein-coupled receptor L pathways were more prominent during the secondary myogenesis phase (E6–E12). Notably, a significant shift in communication patterns was observed from E12 to E18, marked by a reduction in developmental pathway signaling and an increase in immune-related communications. By P30, the communication network had stabilized into a homeostatic state, centered on interactions among myofibers, stromal cells, and the vascular system. This comprehensive atlas of intercellular communication offers novel insights into the signaling dynamics underpinning chicken skeletal muscle development. Full article

Shear-wave velocity (V_s) estimation from the Standard Penetration Test (SPT) can support preliminary site characterization when direct geophysical data are limited, but empirical correlations require validation schemes that reflect transferability between sites. This study evaluates V_s prediction using an interval-paired dataset derived from geotechnical investigations of school foundations in Piura, Peru. Its novelty lies in comparing the raw SPT blow count (NSPT) and the overburden- and energy-corrected SPT blow count ((N₁)₆₀) on the same strict common sample, using grouped cross-validation by school, thereby emphasizing transferability across sites rather than only internal fit. Five predictive scenarios were tested, from penetration-only formulations to geotechnically enriched specifications. The lowest grouped out-of-fold error among the evaluated models was obtained by a generalized power baseline using (N₁)₆₀ and the integral geotechnical predictor set, yielding root mean square error (RMSE) = 80.48 m/s, mean absolute error (MAE) = 60.15 m/s, and coefficient of determination (R²) = 0.338. This moderate R² indicates limited standalone predictive capacity under transfer to unseen schools; therefore, the model is interpreted as a preliminary transfer-oriented correlation rather than as a substitute for direct V_s measurements or as an independent design equation. In the complementary full-data analysis, the strongest descriptive fit was obtained with Hist Gradient Boosting, whereas the strongest explicit equation corresponded to the log-semi baseline. Overall, the findings show that externally validated transferability, descriptive full-data fit, and equation-based interpretability represent different analytical roles in V_s-SPT modeling. Full article

Background: Long COVID (LC) has been associated with persistent endothelial dysfunction and vascular impairment. Although nutrition is a key modifiable determinant of cardiovascular health, the relationship between dietary macronutrient intake and vascular alterations in LC remains poorly understood. Objective: To evaluate the association between dietary macronutrient intake and markers of vascular structure, arterial stiffness, and vascular aging in patients with LC, including potential sex differences. Methods: We conducted a cross-sectional study including 304 patients with LC. Dietary intake was assessed using a validated 7-day dietary record (EVIDENT study). Vascular evaluation included carotid intima–media thickness (cIMT), carotid–femoral pulse wave velocity (cfPWV), brachial–ankle pulse wave velocity (baPWV), cardio-ankle vascular index (CAVI), augmentation index adjusted to a heart rate of 75 beats per minute (AIx@75), and vascular aging index (VAI), measured using carotid ultrasound and validated devices (SphygmoCor^® and VaSera^®). Results: The mean age was 53 ± 12, higher in men (p = 0.001). The study included 207 women (68%) and 97 men (32%). Energy intake and carbohydrate intake in g/day showed a negative association with cfPWV in Model 2 (energy intake: β = −0.06; 95% CI: −0.11 to −0.01; p = 0.02; carbohydrate intake: β = −0.47; 95% CI: −0.87 to −0.07; p = 0.02). The percentage of carbohydrate/total energy intake was positively associated with AIx@75 in Model 2 (β = 0.8; 95% CI 0.12 to 1.49; p = 0.02), and percentage of fat/total energy intake showed a consistent inverse association (β = −0.30; 95% CI: −0.49 to −0.11; p = 0.002). No significant associations were observed for cIMT, baPWV, CAVI or VAI. Conclusions: In patients with LC, total energy intake and absolute carbohydrate intake were negatively associated with cfPWV, whereas the relative contribution of carbohydrates and fats to total energy intake showed divergent associations with AIx@75. These findings suggest that both absolute macronutrient intake and relative macronutrient distribution may be related to central arterial stiffness and wave reflection parameters LC. However, given the cross-sectional design of the study, these results should be interpreted as exploratory and do not allow causal inference. Further longitudinal and interventional studies are needed to confirm these findings and to assess whether nutritional strategies may contribute to modulating vascular risk in this population. Full article

Parkinson’s disease (PD) is a neurodegenerative disorder with an increasing incidence rate that significantly affects patients’ motor functions and quality of life. Involuntary upper limb tremors (ULTs) commonly manifest unilaterally, affecting either the left or right upper limb. Clinically, ULT frequencies can be categorized into three distinct classes: low-frequency (<4.0 Hz), mid-frequency (4.0–7.0 Hz), and high-frequency (>7.0 Hz) tremors. These tremor motions are characterized by oscillatory or rotational (angular displacement) movements, commonly referred to as the micro-Doppler effect (mDE). This study aims to develop a short-range (<1.0 m) and contactless sensing method for ULT detection based on Doppler millimeter-wave (mm-Wave) radar. The reflected electromagnetic waves indicate time-varying frequency characteristics, which can be analyzed by using time–frequency transform (TFT) methods, such as the Wigner–Ville distribution (WVD) and smoothed pseudo WVD (SPWVD). These TFT methods are employed to extract mDE features, which are subsequently visualized as color-coded spectrograms for ULT classification. Then, a two-dimensional (2D) convolutional neural network (CNN) is employed to automatically recognize the visual feature patterns for ULTs classification based on frequency and amplitude information. In the experimental setup, the W-band (76–81 GHz) Doppler mm-Wave biosensor is implemented for sensing and extracting feature patterns. The proposed classifiers based on “WVD + 2D CNN” and “SPWVD + 2D CNN” are trained and validated by using the collected datasets, with 60% randomly selected for training datasets and 40% for testing datasets in each fold validation. A 10-fold cross-validation method is applied to evaluate the classifier’s performances, achieving an average precision of 95.92 ± 0.60%, average recall of 95.89 ± 0.62%, average F1-score of 0.9588 ± 0.0060, and average accuracy of 95.89 ± 0.62%, respectively. The experimental results demonstrate the feasibility of the proposed classifier for real-time ULTs classification in PD patients using short-range (<1.0 m) and contactless sensing. Full article

To evaluate the influence of near-field ground scattering on ultra-wideband (UWB) fuze performance, this paper presents an efficient operator-expansion time-domain physical optics (OE-TD-PO) framework. This method extends conventional far-field TD-PO to electrically large, dispersive rough surfaces under near-field excitation. By leveraging the local plane wave approximation (LPA) and time-domain Kirchhoff approximation (KA), the complex scattering process is decomposed into independent element-wise responses, which reduces the coupling between geometry and wave propagation. The scattering physics of each facet are represented using closed-form material and geometric operators. The material operator accounts for frequency-dependent dispersion and polarimetric reflection, while the geometric operator models intra-facet delay spread in the time domain. An excitation-order expansion of the transient dipole radiation formula is introduced to decouple the source waveform from spatial facet loops, yielding radiation, induction, and static components corresponding to the derivative, proportional, and integral terms of the excitation signal. This decoupling reduces computational complexity while preserving physical fidelity. Validated against analytical and numerical benchmarks, the proposed method effectively quantifies terrain-induced ranging biases and initiation reliability, providing a rigorous basis for adaptive error compensation and gain control in UWB fuzes across diverse environments. Full article

Pulse wave propagation through blood vessels is affected by many biophysical parameters that change with aging. The aim of this study was to investigate both theoretically and experimentally how the pulse wave velocity changes in the vertical position and to introduce a new parameter in biophysics: pulse wave acceleration (PWA). Using a biophysical model of the cardiovascular system, placed in horizontal and vertical positions, pressure waveforms were measured along the arterial tree at several sites at different diastolic pressures and pump frequencies. Blood flow waveforms on the carotid and femoral arteries in the supine and standing positions were measured on the subjects. The results showed that the pulse pressure wave accelerates in the direction of gravity and decelerates in the opposite direction both in the model and in humans. A new biophysical parameter, PWA, was defined, and the experimental results are in agreement with the mathematical model. Due to the acceleration of the pulse wave, the reflected wave in the standing position arrives earlier in systole and contributes to the increase in pressure. This emerging biophysical parameter may contribute to a better understanding of the phenomenon of wave propagation of blood through blood vessels. Full article

Modern connected and automated vehicles are equipped with various onboard sensors, which continuously generate high-rate perception data. The reliable and timely sharing of such sensor data among neighboring vehicles requires high-capacity and low-latency vehicle-to-vehicle (V2V) communications. Millimeter-wave (mmWave) technology is a promising solution for supporting such high-rate transmission. However, mmWave V2V communication may be severely affected by non-line-of-sight (NLOS) blockage caused by limited transmission range, roadside obstacles, and moving vehicles. Relay forwarding can improve communication reliability and extend transmission distance, while intelligent reflecting surfaces (IRSs) can construct virtual line-of-sight (LOS) links to mitigate NLOS blockage. In this paper, we propose deploying IRSs on urban roadsides to improve mmWave multi-hop V2V communication for vehicular sensor-data sharing by integrating IRS-assisted link selection into multi-hop relay forwarding. However, IRS deployment introduces new challenges in relay selection and directional transmission coordination under interference. To address these challenges, we propose an IRS allocation and relay selection (IARS) scheme for IRS-assisted multi-hop V2V communication. The proposed scheme is based on a transmission evaluation function that jointly considers inter-vehicle distance, link quality, and concurrent transmissions. Simulation results show that the proposed IARS scheme can effectively improve communication reliability and reduce multi-hop delay, thereby supporting reliable and timely sensor-data sharing in urban vehicular networks. Full article

Hydrothermal alteration mapping is a critical component of porphyry copper exploration because alteration assemblages provide important vectors toward mineralization. This study presents a systematic evaluation of supervised machine learning algorithms for delineating hydrothermal alteration zones using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) short-wave infrared (SWIR) surface reflectance data (AST_07XT). The investigation focuses on the Nain region within the central Urumieh–Dokhtar Magmatic Arc (UDMA), Iran, a major metallogenic belt hosting numerous porphyry copper systems. Representative spectral endmembers corresponding to Al–OH-bearing and Mg–OH-bearing hydrothermal alteration minerals were extracted using Minimum Noise Fraction (MNF), Pixel Purity Index (PPI), and n-dimensional visualization techniques. These endmembers were subsequently used to train and evaluate a comprehensive suite of supervised machine learning classifiers, including linear, kernel-based, tree-based, ensemble, probabilistic, boosting, and neural-network algorithms for pixel-wise hydrothermal alteration mapping. Model performance was evaluated using multiple statistical metrics, including overall accuracy (OA), average accuracy (AA), precision, recall, F1-score, Cohen’s kappa coefficient, area under the ROC curve (AUC), spatial cross-validation accuracy, uncertainty analysis, and spatial agreement analysis. Among the evaluated classifiers, SVM_Linear, SVM_RBF, LDA, and MLP achieved the highest classification performance, with overall accuracies exceeding 94% and strong spatial consistency between classified maps. The resulting alteration maps display spatially coherent distributions of Al–OH and Mg–OH minerals that are consistent with established hydrothermal alteration zoning models in porphyry–epithermal systems. The mapped hydrothermal alteration zones show strong spatial correspondence with known mineralized areas and alteration patterns within the Urumieh–Dokhtar Magmatic Arc, confirming the geological reliability of the classification results. Uncertainty analysis further indicates high model confidence across most alteration zones, with higher uncertainty values mainly restricted to transitional and spectrally heterogeneous regions. The results demonstrate that integrating ASTER SWIR imagery with supervised machine learning algorithms provides a robust, scalable, and transferable framework for regional-scale hydrothermal alteration mapping and mineral exploration in porphyry copper provinces. Full article

A numerical study of shock wave propagation through multiple raindrops is presented using a density-based compressible two-phase flow solver coupled with a sharp-interface volume-of-fluid (VoF) method. The piecewise linear interface calculation (PLIC) approach is employed to reconstruct gas–liquid interfaces and capture droplet deformation during shock interaction. The numerical framework is first validated using a one-dimensional gas–liquid shock tube problem and a shock–helium bubble interaction benchmark. The method is then applied to investigate shock interactions with single, double, and multiple raindrops under compressible flow conditions. Numerical results show that complex wave structures, including shock reflection, diffraction, and wave interference, develop during shock propagation through raindrop fields. Interactions between neighboring droplets lead to local pressure amplification and non-uniform flow structures. Full article

Background: In increasingly dynamic and uncertain organizational environments, employees’ proactive behavior—characterized by self-initiation, future orientation, and change orientation—is critical for organizational adaptability and long-term competitiveness. Prior research has primarily examined how externally provided job resources stimulate proactive behavior. More recent work has begun to consider employees’ personal resources, but it largely adopts a capability level-based view, conceptualizing them as self-evaluations of individuals’ ability to control and influence their environment. This focus overlooks capability malleability-based personal resources that shape more fundamental beliefs about the malleability of human capability. Objective: Drawing on the job demands–resources (JD–R) model, this study investigates how employees’ growth mindset—reflecting beliefs that human capability can be developed—promotes proactive behavior by alleviating workplace anxiety, an anticipatory emotional state rooted in concerns about future work-related threats. We further examine time pressure as a key boundary condition. Method: A three-wave, multisource survey design was employed, collecting data from 326 employee–supervisor dyads. Results: The results show that employees’ growth mindset is negatively associated with workplace anxiety, which in turn positively predicts proactive behavior. Moreover, time pressure strengthens both the anxiety-buffering effect of growth mindset and the indirect effect of growth mindset on proactive behavior via workplace anxiety. Conclusions: By incorporating capability malleability-based personal resources into the JD–R model, this study advances understanding of the antecedents of proactive behavior beyond capability level-based self-evaluations toward deeper beliefs about the malleability of human capability. Applications: This study offers practical implications for managers seeking to cultivate employee proactivity. Full article