Barium titanate piezoelectric-film-based beam-array airflow sensor for wearable breath-monitoring application

This study presents a barium titanate (BaTiO3) film-based piezoelectric airflow sensor. This sensor integrated a piezoelectric beam array with a poly(dimethylsiloxane) orifice membrane as the core sensing component. The compact size of the micromachined device fit the requirements for a wearable device. The hydrothermally grown barium titanate film exhibited an orthorhombic crystal structure with good piezoelectric properties. We propose an algorithm to determine the airflow sensor performance using data from the measured piezoelectric signal and the displacement of the piezoelectric beam. This algorithm correlates the discharge coefficient of the core sensing component, Reynold’s number, airflow velocity, pressure difference across the component, displacement of the piezoelectric beam, strain of the barium titanate film, and generated charge from the sensor, which is rarely reported in the literature. The Young’s modulus and piezoelectric constant of the barium titanate film could also be derived as 100 GPa and 8 pC/N, respectively. Utilizing this algorithm and the generated piezoelectric signal of the sensor, important breath parameters of a young male subject at rest were monitored.

Ambient seismic noise interferometry using rotational ground motion

<p>In seismology, new sensing technologies are currently emerging that can measure ground motion beyond the conventional seismic translation measurements. In particular, rotational motion sensors record an additional 3 components of ground motion and thus provide access to additional information about the seismic wavefield. </p><p>So far, most studies of rotational ground motion are mainly based on recordings of earthquakes or active sources. In this study, we push the limit towards the very weak motions associated with ocean-generated ambient seismic noise. Our aim is to show the potential of using these measurements in the context of ambient noise interferometry. </p><p>We use recordings from two ring lasers in Germany: the `G-Ring' at the Wettzell Geodetic Observatory, and `ROMY' at the Fürstenfeldbruck Observatory near Munich, at a distance of approximately 160 km. These are the most sensitive instruments to date which offer a local, direct measurement of rotational ground motion. </p><p>We demonstrate that the sensitivity of the Wettzell instrument has been sufficiently improved to detect Love waves in the primary microseismic frequency band. Both the G-Ring and ROMY ring lasers are also capable of detecting Love waves in the stronger secondary microseismic band. This latter frequency range is used to test the possibility of performing noise interferometry with rotational records. </p><p>The first results of rotational noise interferometry between the two ring lasers are promising. The correlation waveform is verified by comparison with interferometry carried out with co-located seismometer data at both locations, as well as with numerical simulations. </p><p>In conclusion, we show that ambient noise interferometry is in principle feasible using real rotational recordings of ocean-generated noise. This proof of concept study forms a first step towards noise interferometery of 6-component displacement data. </p>

Audio-visual interactions in egocentric distance perception: Ventriloquism effect and aftereffect

The processes of audio-visual integration and of visually-guided re-calibration of auditory distance perception are not well understood. Here, the ventriloquism effect (VE) and aftereffect (VAE) were used to study these processes in a real reverberant environment. Auditory and audio-visual (AV) stimuli were presented, in interleaved trials, over a range of distances from 0.7 to 2.04 m in front of the listener, whose task was to judge the distance of auditory stimuli or of the auditory components of AV stimuli. The relative location of the visual and auditory components of AV stimuli was fixed within a session such that the visual component was presented from distance 30% closer (V-closer) than the auditory component, 30% farther (V-farther), or aligned (V-aligned). The study examined the strength of VE and VAE as a function of the reference distance and of the direction of the visual component displacement, and the temporal profile of the build-up/break-down of these effects. All observed effects were approximately independent of target distance when expressed in logarithmic units. The VE strength, measured in the AV trials, was roughly constant for both directions of visual-component displacement such that, on average, the responses shifted in the direction of the visual component by 72% of the audio-visual disparity. The VAE strength, measured on the interleaved auditory-only trials, was stronger in the V-farther than the V-closer condition (44% vs. 31% of the audio-visual disparity, respectively). The VAE persisted to post-adaptation auditory-only blocks of trials, however it was weaker and the V-farther/V-closer asymmetry was reduced. The rates of build-up/break-down of the VAE were also asymmetrical, with slower adaptation in the V-closer condition. These results suggest that, on a logarithmic scale, the AV distance integration is symmetrical, independent of the direction of induced shift, while the visually-induced auditory distance re-callibration is asymmetrical, stronger and faster when evoked by more distant visual stimuli.

Imaging of Hip Arthroplasties: Normal Findings and Hardware Complications

Hip arthroplasty is a common and largely successful surgical procedure, often used for the treatment of advanced osteoarthritis. Imaging plays a key role in routine postoperative imaging surveillance as well as the evaluation of post-arthroplasty pain. Radiographs are the first-line imaging modality and may be followed by computed tomography (CT), ultrasound, and/or magnetic resonance imaging (MRI). Recent advancements in imaging techniques allow for metal artifact reduction on CT and MRI. A variety of complications can arise in the setting of arthroplasty: mechanical loosening, component wear-induced synovitis and osteolysis, adverse local tissue reaction, infection, periprosthetic fracture, implant dislocation and/or component displacement, tendinopathy, and neurovascular injury. This article reviews normal and abnormal imaging findings of hip arthroplasty.

Landslide displacement prediction using the GA-LSSVM model and time series analysis: a case study of Three Gorges Reservoir, China

Predicting landslide displacement is challenging, but accurate predictions can prevent casualties and economic losses. Many factors can affect the deformation of a landslide, including the geological conditions, rainfall and reservoir water level. Time series analysis was used to decompose the cumulative displacement of landslide into a trend component and a periodic component. Then the least-squares support vector machine (LSSVM) model and genetic algorithm (GA) were used to predict landslide displacement, and we selected a representative landslide with episodic movement deformation as a case study. The trend component displacement, which is associated with the geological conditions, was predicted using a polynomial function, and the periodic component displacement which is associated with external environmental factors, was predicted using the GA-LSSVM model. Furthermore, based on a comparison of the results of the GA-LSSVM model and those of other models, the GA-LSSVM model was superior to other models in predicting landslide displacement, with the smallest root mean square error (RMSE) of 62.4146 mm, mean absolute error (MAE) of 53.0048 mm and mean absolute percentage error (MAPE) of 1.492 % at monitoring station ZG85, while these three values are 87.7215 mm, 74.0601 mm and 1.703 % at ZG86 and 49.0485 mm, 48.5392 mm and 3.131 % at ZG87. The results of the case study suggest that the model can provide good consistency between measured displacement and predicted displacement, and periodic displacement exhibited good agreement with trends in the major influencing factors.

Landslide displacement prediction using the GA-LSSVM model and time series analysis: a case study of Three Gorges Reservoir, China

Predicting landslide displacement is challenging, but accurate predictions can prevent casualties and economic losses. Many factors can affect the deformation of a landslide, including the geological conditions, rainfall, and reservoir water level. Time series analysis was used to decompose the cumulative displacement of landslide into a trend component and a periodic component. Then the least squares support vector machine (LSSVM) model and genetic algorithm (GA) were used to predict landslide displacement, and we selected a representative landslide with step-like deformation as a case study. The trend component displacement, which is associated with the geological conditions, was predicted using a polynomial function, and the periodic component displacement which is associated with external environmental factors, was predicted using the GA-LSSVM model. Furthermore, based on a comparison of the results of the GA-LSSVM model and those of other models, the GA-LSSVM model was superior to other models in predicting landslide displacement, with the smallest root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The results of the case study suggest that the model can provide good consistency between measured displacement and predicted displacement, and periodic displacement exhibited good agreement with trends in the major influencing factors.

An Analytical Solution for Three-Component, Two-Phase Surfactant Flooding Dependent on the Hydrophilic/Lipophilic-Difference Equation and the Net-Average-Curvature Equation of State

Summary Microemulsion phase behavior is crucial to surfactant flooding performance and design. In previous studies, analytical/numerical solutions for surfactant flooding were developed dependent on the classical theory of multicomponent/multiphase displacement and empirical microemulsion phase-behavior models. These phase-behavior models were derived from empirical correlations for component-partition coefficients or from the Hand-rule model (Hand 1930), which empirically represents the ternary-phase diagram. These models may lack accuracy or predictive abilities, which may lead to improper formulation design or unreliable recovery predictions. To provide a more-insightful understanding of the mechanisms of surfactant flooding, we introduced a novel microemulsion phase-behavior equation of state (EOS) dependent on the hydrophilic/lipophilic-difference (HLD) equation and the net-average curvature (NAC) model, which is called HLD-NAC EOS hereafter. An analytical model for surfactant flooding was developed dependent on coherence theory and this novel HLD-NAC EOS for two-phase three-component displacement. Composition routes, component profile along the core, and oil recovery can be determined from the analytical solution. The analytical solution was validated against numerical simulation as well as experimental study. This HLD-NAC EOS based analytical solution enables a systematic study of the effects of phase-behavior-dependent variables on surfactant-flooding performance. The effects of solution gas and pressure on microemulsion phase behavior were investigated. It was found that an increase of solution gas and pressure would lead to enlarged microemulsion bank and narrowed oil bank. For a surfactant formulation designed at standard conditions, the analytical solution was able to quantitatively predict its performance under reservoir conditions.