Search Results (110)

Small-scale two-phase flows are subject to intense capillary accelerations that must be treated with care in order to avoid artifacts often associated with the numerical methodologies used, such as excessive fragmentation of structures. This analysis proposes a formulation of capillary actions for compressible viscous two-phase flows within the framework of discrete mechanics, where the concept of mass is abandoned in favor of a law of motion that describes the conservation of accelerations, one related to inertia and the other to external actions. With the introduction of the capillary term, the sum of a capillary potential gradient and the dual curl of a vector potential is consistent with the other terms of the law of motion, a formal Helmholtz–Hodge decomposition. This fully compressible formulation reproduces the capillary waves generated by the source terms and the contact and shock discontinuities in the two immiscible fluids. This methodology completely eliminates parasitic currents due mainly to the presence of residual curl in the capillary source terms. Several classic examples demonstrate the validity of this approach. Full article

In the marine environment, numerous factors endanger the preservation of underwater rock surfaces as well as submerged archeological artifacts, including physical, chemical, and biological processes. Limestone and marble are common materials used in artifacts due to their availability and long-term durability. However, such surfaces provide a suitable substrate for the settlement of micro- and macro-organisms, causing so-called biofouling, which significantly contributes to stone deterioration. Previous studies have demonstrated the applicability of antifouling coatings containing ionic liquids (ILs) on marble surfaces and assessed their durability for up to 15 days under submerged environments. To further corroborate these results, additional physical studies (colorimetric, contact angles, capillarity water absorption measurements, and UV aging) were carried out on treated limestone. Washout tests were also performed on both lithotypes to verify the coatings’ stability under medium-term underwater exposures. The results of these investigations are reported here. Our data confirm that the application of IL-based coatings had no effect on the intrinsic properties of the limestone surfaces, as previously reported for marble, including resistance to daily UV irradiation. In addition, laboratory tests demonstrated good coating durability against seawater erosive action for up to 6 months. Full article

Drowsy driving and sudden medical emergencies are major contributors to traffic accidents, necessitating continuous, non-intrusive driver monitoring. Since current technologies often struggle to balance accuracy with practicality, this study presents the design, fabrication, and validation of a smart steering wheel prototype. The device integrates dry-contact electrocardiogram (ECG), photoplethysmography (PPG), and inertial sensors to facilitate multimodal physiological monitoring. The system underwent a two-stage evaluation involving a single participant: laboratory validation benchmarking acquired signals against medical-grade equipment, followed by real-world testing in a custom electric research vehicle to assess performance under dynamic conditions. Laboratory results demonstrated that the prototype captured high-quality signals suitable for reliable heart rate variability analysis. Furthermore, on-road evaluation confirmed the system’s operational functionality; despite increased noise from motion artifacts, the ECG signal remained sufficiently robust for continuous R-peak detection. These findings confirm that the multimodal smart steering wheel is a feasible solution for unobtrusive driver monitoring. This integrated platform provides a solid foundation for developing sophisticated machine-learning algorithms to enhance road safety by predicting fatigue and detecting adverse health events. Full article

At-rest PPG signals have been explored for detecting atrial fibrillation (AF), yet current signal-processing techniques do not achieve perfect accuracy even under low-motion artifact (MA) conditions. This study evaluates the effectiveness of a single-degree-of-freedom time–frequency (SDOF-TF) method in analyzing at-rest PPG signals for AF detection. The method leverages the influence of MA on the instant parameters of each harmonic, which is identified using an SDOF model in which the tissue–contact–sensor (TCS) stack is treated as an SDOF system. In this model, MA induces baseline drift and time-varying system parameters. The SDOF-TF method enables the quantification and removal of MA and noise, allowing for the accurate extraction of the arterial pulse waveform, heart rate (HR), heart rate variability (HRV), respiration rate (RR), and respiration modulation (RM). Using data from the MIMIC PERform AF dataset, the method achieved 100% accuracy in distinguishing AF from non-AF cases based on three features: (1) RM, (2) HRV derived from instant frequency and instant initial phase, and (3) standard deviation of HR across harmonics. Compared with non-AF, the RM for each harmonic was increased by AF. RM exhibited an increasing trend with harmonic order in non-AF subjects, whereas this trend was diminished in AF subjects. Full article

This article reviews recent advances in microwave and radar techniques for non-contact measurement of human vital signs in dynamic conditions. The focus is on solutions that work when the subject is moving or performing everyday activities, rather than lying motionless in clinical settings. This review covers innovative biodegradable and flexible antenna designs for wearable devices operating in multiple frequency bands and supporting efficient 5G/IoT connectivity. Particular attention is paid to ultra-wideband (UWB) radar, Doppler sensors, and microwave reflectometry combined with advanced signal-processing and deep learning algorithms for robust estimation of respiration, heart rate, and other cardiopulmonary parameters in the presence of body motion. Applications in telemedicine, home monitoring, sports, and search and rescue are discussed, including localization of people trapped under rubble by detecting their vital sign signatures at a distance. This paper also highlights key challenges such as inter-subject anatomical variability, motion artifacts, hardware miniaturization, and energy efficiency, which still limit widespread deployment. Finally, related developments in microwave imaging and early detection of pathological tissue changes are briefly outlined, highlighting the shared components and processing methods. In general, microwave techniques show strong potential for unobtrusive, continuous, and environmentally sustainable monitoring of human physiological activity, supporting future healthcare and safety systems. Full article

The Jingbaoer Grassland Jade Mine situated approximately 20 km northwest of Mazongshan Town in Gansu Province, China, represents an important source of nephrite dating back to the pre-Qin period. In this study, 58 representative nephrite samples were analyzed to investigate their mineralogical and geochemical characteristics using polarized light microscopy, scanning electron microscopy (SEM), electron probe microanalysis (EPMA), and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). The mine is situated near the contact zone between the Silurian Gongpoquan Group and Devonian granite, with surrounding rocks primarily consisting of Precambrian dolomitic marble. The nephrite displays diverse colors—white, bluish-white, sugar-white, and cyan—with darker tones and abundant manganese-stained dendritic and flocculent inclusions. It shows a relative density of 2.82–2.99, a refractive index of 1.60–1.62, and a vitreous to greasy luster. Texturally, the jade is predominantly composed of micro-fibrous interwoven tremolite, occasionally exhibiting oriented recrystallization textures. Minor minerals include diopside, apatite, titanite, chlorite, epidote, allanite, rutile, and graphite. Chemically, the samples are rich in SiO₂, MgO, and CaO, with trace amounts of FeO, MnO, Al₂O₃, and Na₂O. Notably, Sr and Sm are enriched, Nb is slightly depleted, and Eu shows a distinct negative anomaly. The average total rare earth content is 4.25 µg/g. The study suggests that the deposits in the research area are typical of the contact-metasomatic type, formed through multi-stage hydrothermal metasomatism between acidic granitic intrusions and dolomitic marble, creating favorable conditions for the formation of high-quality tremolite jade. Comparative analysis with jade artifacts excavated from the Tomb of Marquis Yi of Zeng suggests a possible provenance link to the Jingbaoer deposit, providing valuable evidence for the historical mining and distribution of nephrite during the Warring States period. Full article

Motion artifacts (MA) are a key factor affecting the accuracy of a measured arterial pulse signal at rest. This paper presents a generalized time–frequency method for MA removal that is built upon a single-degree-of-freedom (SDOF) model of MA, where MA is manifested as time-varying system parameters (TVSPs) of the SDOF system for the tissue–contact-sensor (TCS) stack between an artery and a sensor. This model distinguishes the effects of MA and respiration on the instant parameters of harmonics in a measured pulse signal. Accordingly, a generalized SDOF-model-based time–frequency (SDOF-TF) method is developed to obtain the instant parameters of each harmonic in a measured pulse signal. These instant parameters are utilized to reconstruct the pulse signal with MA removal and extract heart rate (HR) and respiration parameters. The method is applied to analyze seven measured pulse signals at rest under different physiological conditions using a tactile sensor and a PPG sensor. Some observed differences between these conditions are validated with the related findings in the literature. As compared to instant frequency, the instant initial phase of a harmonic extracts respiration parameters with better accuracy. Since HR variability (HRV) affects arterial pulse waveform (APW), the extracted APW with a constant HR serves better for deriving arterial indices. Full article

Investigating the pore structure and understanding the relationship between pore characteristics and mechanical properties are crucial to research in the study of cement mortar. At present, the segmentation of large-scale concrete pores is mainly conducted using traditional algorithms or software, which are time-consuming and operate in a semi-automated manner. However, the application of these methods faces challenges when analyzing large-scale rock pores due to factors such as a lack of data, artifacts, and inconsistent contrast. In this study, six series of cement mortars were subjected to real-time CT scanning under uniaxial loading (RT-CT) to collect real-time three-dimensional data on the evolution of pore structures during loading. To address issues such as artifacts and inconsistent contrast, a new augmentation method was proposed to overcome artifacts and enhance contrast consistency. Finally, the augmented dataset was utilized for training, and the Fast R-CNN algorithm served as the framework for developing the pore recognition model. The results indicate that the improved algorithm demonstrates enhanced convergence and greater accuracy in pore segmentation. A mathematical model is developed to relate uniaxial compressive strength (UCS) to pore fractal dimension and porosity, based on pore segmentation analysis. The fractal dimensions evolution of each specimen is consistent with the progressive failure indicated by the strain-stress curve. Under uniaxial loading, specimens with a 4:1 cement–sand ratio exhibited peak strength. The incorporation of fractals improved particle contact, thereby facilitating the formation of the skeletal structure. These efforts contribute to improving the identification of the deformation of cement mortars. Full article

The integration of flexible materials with optical sensing technologies has advanced wearable optical biosensors, offering significant potential in personalized medicine, health monitoring, and disease prevention. This review summarizes the recent advancements in flexible materials for wearable optical biosensors, with a focus on materials such as polymer substrates, nanostructured materials, MXenes, hydrogels, and textile-based integrated platforms. These materials enhance the functionality, sensitivity, and adaptability of sensors, particularly in wearable applications. The review also explores various optical sensing mechanisms, including surface plasmon resonance (SPR), optical fiber sensing, fluorescence sensing, chemiluminescence, and surface-enhanced Raman spectroscopy (SERS), emphasizing their role in improving the detection capabilities for biomarkers, physiological parameters, and environmental pollutants. Despite significant advancements, critical challenges remain in the fabrication and practical deployment of flexible optical biosensors, particularly regarding the long-term stability of materials under dynamic environments, maintaining reliable biocompatibility during prolonged skin contact, and minimizing signal interference caused by motion artifacts and environmental fluctuations. Addressing these issues is vital to ensure robustness and accuracy in real-world applications. Looking forward, future research should emphasize the development of multifunctional and miniaturized devices, the integration of wireless communication and intelligent data analytics, and the improvement of environmental resilience. Such innovations are expected to accelerate the transition of flexible optical biosensors from laboratory research to practical clinical and consumer healthcare applications, paving the way for intelligent health management and early disease diagnostics. Overall, flexible optical biosensors hold great promise in personalized health management, early disease diagnosis, and continuous physiological monitoring, with the potential to revolutionize the healthcare sector. Full article

This paper, the first of two parts, presents an analytical model of motion artifacts (MAs) in measured pulse signals by accelerometers and photoplethysmography (PPG) sensors. As the transmission path from the true pulse signal in an artery to the sensor output (measured pulse signal), the tissue–contact–sensor (TCS) stack is modeled as a 1DOF (degree-of-freedom) system. MAs cause baseline drift of the mass and simultaneously time-varying system parameters (TVSPs) of the TCS stack. With arterial wall displacement and pulsatile pressure serving separately as the true pulse signal, an analytical model is developed to mathematically relate baseline drift and TVSP to a measured pulse signal. With assumed values of baseline drift and TVSPs, the numerical calculation is conducted in MATLAB. While baseline drift is low-frequency additive noise and can greatly swing a measured pulse signal, TVSP generates relatively small, abrupt distortion (e.g., 1% variation in heart rate and <5% change in pulse amplitude) but rides on each harmonic of the true pulse signal. By taking into account the full involvement of the transmission path in pulse measurement, this analytical model serves as a fundamental framework for quantifying baseline drift and TVSPs from a measured pulse signal in the future. Full article

This paper, the second of two parts, presents an analytical model of motion artifacts (MA) in measured pulse signals by a tactile sensor, which contains a deformable microstructure sitting on a substrate. While the tissue-contact-sensor (TCS) stack and the sensor are both treated as a 1DOF (degree-of-freedom) system, tissue–sensor contact joins their mass together to form a 1DOF system with springs and dampers on both sides. MA on the sensor substrate causes baseline drift and time-varying system parameters (TVSP) of the TCS stack simultaneously. An analytical model is developed to mathematically relate baseline drift and TVSP to a measured pulse signal. The numerical calculation is conducted in MATLAB. Baseline drift in a measured pulse signal is much lower than the actual MA in its measurement. As compared to baseline drift, TVSP generates relatively abrupt, small distortion (e.g., 0.2% variation in heart rate and <5% change in pulse amplitude), but it rides on each harmonic of the true pulse signal. Sensor design alters both the deviation of the amplitude and waveform of a measured pulse signal from the true pulse signal and the influence of MA on it. Full article

Motion artifacts (MA) cause great variability in a measured arterial pulse signal, and treatment of MA solely as a baseline drift (BD) fails to eliminate its effect on the measured signal. This paper presents a study on the effect of MA at rest (<0.7 Hz) on measured arterial pulse signals using a microfluidic-based tactile sensor. By taking full account of the dynamic behavior of the transmission path from the true pulse signal in an artery to a measured pulse signal at the sensor, the tissue-contact-sensor (TCS) stack, an analytical model of MA in a measured pulse signal is developed. In this model, the TCS stack is treated as a 1DOF system for its dynamic behavior; MA is quantified as the displacement (i.e., BD) and time-varying system parameters (TVSP) of the TCS stack. The mathematical expression of MA in a measured pulse signal reveals that while BD remains as low-frequency additive noise, TVSP causes time-varying harmonics in a measured pulse signal. Further time-frequency analysis (TFA) of measured pulse signals validates the existence of TVSP and, for the first time, reveals its effect on a measured pulse signal: time-varying amplitude in each harmonic and non-flat harmonic-MA-coupled baseline. Full article

Remote photoplethysmography (rPPG) offers a promising solution for non-contact driver monitoring by detecting subtle blood flow-induced facial color changes from video. However, motion artifacts in dynamic driving environments remain key challenges. This study presents an rPPG framework that combines signal processing techniques before and after applying Eulerian Video Magnification (EVM) for pulse rate (PR) estimation in driving simulators. While not novel, the approach offers insights into the efficiency of the EVM method and its time complexity. We compare results of the proposed rPPG approach against reference Empatica E4 data and also compare it with existing achievements from the literature. Additionally, the possible bias of the Empatica E4 is further assessed using an independent dataset with both the Empatica E4 and the Faros 360 measurements. EVM slightly improves PR estimation, reducing the mean absolute error (MAE) from 6.48 bpm to 5.04 bpm (the lowest MAE (~2 bpm) was achieved under strict conditions) with an additional time required for EVM of about 20 s for 30 s sequence. Furthermore, statistically significant differences are identified between younger and older drivers in both reference and rPPG data. Our findings demonstrate the feasibility of using rPPG-based PR monitoring, encouraging further research in driving simulations. Full article

Line laser measurement, as a typical method of laser triangulation, makes the acquisition of 3D tooth-surface data more accurate, efficient, and informative. Thus, a line laser 3D measurement model of gears is established, and a specialized polyhedral artifact with specific geometric features is invented to determine the pose parameters of the line laser sensor in measuring space. Based on this, a single-spindle gear-measuring instrument is developed and a series of experimental studies are conducted for gears with different module and flank directions in this instrument, including profile deviation, helix deviation, pitch deviation, topological deviation, etc. A comparative experiment with traditional contact measurement methods validates the correctness of the methods mentioned in this paper for the accurate evaluation of tested gears. In further research, the mining and utilization of big data obtained from the line laser 3D measurement of gears will be an important topic. Full article

The vehicle scanning method (VSM) is widely used for bridge damage identification (BDI) because it relies solely on vehicle dynamic responses. The recently introduced contact point response, which is derived from vehicle dynamics but devoid of vehicle-related natural frequencies, shows great potential for application in the vehicle scanning method. However, its application in bridge damage detection remains understudied. The aim of this paper is to propose a new bridge damage identification method based on the contact point response. The method uses variational modal decomposition (VMD) to solve the problem of mode mixing and spurious frequencies in the signal. The continuous wavelet transform (CWT) is then utilized for damage identification. The introduction of variational modal decomposition makes the extracted signal more accurate, thus enabling more accurate damage identification. Numerical simulations validate the method’s robustness under varying conditions, including the vehicle speed, wavelet scale factors, the number of bridge spans, and pavement roughness. The results demonstrate that variational modal decomposition eliminates signal artifacts, producing smooth variational modal decomposition–continuous wavelet transform curves for accurate damage detection. In this study, we offer a robust and practical solution for bridge health monitoring using the vehicle scanning method. Full article