Motion Variations Research Articles

Daily and Subdaily Wind and Divergence Variations Observed by a Shipborne Doppler Radar off the Southwestern Coast of Sumatra

Abstract This study investigated the daily cycle of the wind and divergence fields observed off the southwestern coast of Sumatra during a field campaign of the Years of the Maritime Continent pilot study. An algorithm was developed to retrieve kinematic variables from the single-Doppler data collected aboard the Research Vessel Mirai from 24 November to 13 December 2015. The observed daily cycles of the wind and divergence fields consisted of diurnal, semidiurnal, and short-term variations. Diurnal wind variation was characterized by deep and three-dimensional circulation. There was an approximate phase locking of the semidiurnal variation to the diurnal variation, both in the wind and divergence fields. The short-term wind variation occurred at a time scale of ∼1–3 h, and this pattern was associated with density currents or mesoscale gravity waves. Up to 73% of the daily vertical motion variance can be attributed to the diurnal and semidiurnal vertical motion variations with comparable strengths. Concurrently, precipitation propagated offshore in phase with density currents and mesoscale gravity waves. Our results suggest that diurnal and semidiurnal wind variations dominate the daily evolution of precipitation, whereas density currents and mesoscale gravity waves control offshore propagation. Additionally, it appears that the daily precipitation cycle is modulated by multiple-time-scale wind variabilities of less than a day, which is also responsible for the development of strong nocturnal convection off the southwestern coast of Sumatra. Significance Statement To improve our understanding of the daily wind and divergence cycle off the southwestern coast of Sumatra, we examined wind data collected by a shipborne Doppler radar. The observed daily cycles of the wind and divergence fields consisted of diurnal and semidiurnal variations, as well as a 1–3-h variation associated with a density current or mesoscale gravity wave. Our results suggest that diurnal and semidiurnal wind variations dominate the daily evolution of precipitation, whereas density currents and mesoscale gravity waves control offshore propagation. Thus, we highlight the role of multiple-time-scale wind variabilities of less than a day in modulating the daily precipitation cycle off the southwestern coast of Sumatra.

Pengaruh Gerak Pemakanan (Feeding) Terhadap Hasil Kekasaran Permukaan Baja Karbon S45C Pada Proses Mesin CNC Turning SKT 160A

CNC machine is a machine tool whose entire operation is controlled using a computer by inputting N and G codes. There are 2 types of programming for CNC lathes, namely incremental (the starting point of the program can change or the program endpoint becomes the beginning point of the next program), absolute (the starting point becomes the fixed reference point of the program. The study aimed to determine the low surface roughness value of S45C carbon steel with feeding motion variations of 0.15 mm, 0.25 mm, and 0.35 mm with a cutting depth of 0.4 mm and a constant spindle rotation speed. The insert used is branded iscar with a radius of 0.8 mm.. The lowest roughness value at feeding motion of 0.15 mm belongs to the roughness class, namely N7. The intermediate roughness value at feeding motion of 0.25 mm belongs to the roughness class N7. While the highest roughness value in feeding motion (feeding) 0.35 mm is included in the roughness class, namely N8. The average value results are obtained from the testing process using a roughness test tool (stylus).. The effect of feeding motion on the results of the surface roughness of S45C carbon steel in the CNC process is that the smaller the feeding value, the lower the roughness value produced, while the greater the feeding value, the higher the roughness value produced.

FAST skill assessment from kinematics data using convolutional neural networks

FAST is a point of care ultrasound study that evaluates for the presence of free fluid, typically hemoperitoneum in trauma patients. FAST is an essential skill for Emergency Physicians. Thus, it requires objective evaluation tools that can reduce the necessity of direct observation for proficiency assessment. In this work, we use deep neural networks to automatically assess operators' FAST skills. We propose a deep convolutional neural network for FAST proficiency assessment based on motion data. Prior work has shown that operators demonstrate different domain-specific dexterity metrics that can distinguish novices, intermediates, and experts. Therefore, we augment our dataset with this domain knowledge and employ fine-tuning to improve the model's classification capabilities. Our model, however, does not require specific points of interest (POIs) to be defined for scanning. The results show that the proposed deep convolutional neural network can classify FAST proficiency with 87.5% accuracy and 0.884, 0.886, 0.247 sensitivity for novices, intermediates, and experts, respectively. It demonstrates the potential of using kinematics data as an input in FAST skill assessment tasks. We also show that the proposed domain-specific features and region fine-tuning increase the model's classification accuracy and sensitivity. Variations in probe motion at different learning stages can be derived from kinematics data. These variations can be used for automatic and objective skill assessment without prior identification of clinical POIs. The proposed approach can improve the quality and objectivity of FAST proficiency evaluation. Furthermore, skill assessment combining ultrasound images and kinematics data can provide a more rigorous and diversified evaluation than using ultrasound images alone.

THE EFFECTS OF HANDGRIP AND RANGE OF MOTION VARIATIONS ON MUSCLE ACTIVITY IN DIFFERENT DELTOID EXERCISES

This study aimed to compare the effects of handgrip and range of motion (ROM) variations on muscle activity in different deltoid exercises. 14 resistance-trained men volunteered for 1RM and EMG testing with a load corresponding to 80% of 1RM. The subjects performed three different handgrips during Dumbell Front Raise (DFR), two different ROM variations for Dumbell Lateral Raise (DLR), and two different handgrips during Dumbell Rear Delt Raise (DRDR). Electromyogram (EMG) activity was measured in the anterior, medial, and posterior heads of deltoids. For the DFR exercise, the highest mean EMG activity was greater for the anterior deltoid, and the highest activity was observed in pronate grip (PG) con (51.57%). For the anterior deltoid EMG activity was significantly greater in PG con (51.57%) compared with hammer grip (HG) con (43.36%) (p˂0.05). HG ecc activity (40.36%) was significantly greater than PG ecc (36.4%) in posterior deltoid (p˂0.05). For the DLR exercise, the highest activity was observed in medial deltoid, and for limited ROM (LTR) (20.74%). LTR con activity (19.37%) was significantly greater than full ROM con (FLR) (16.88%) in the anterior head (p˂0.05). For the DRDR exercise, the mean activity was greater in medial deltoid, and HG con showed the highest activity (24.47%). The mean electromyography activity for the posterior deltoid was significantly greater in standard grip (STD) ecc compared with HG ecc (17.3%) (p˂0.05). In conclusion, for the heads of the deltoid muscle, the use of the different handgrip and ROM variations may increase neuromuscular activity.

Sea Tide Influence on Ice Flow of David Drygalski’s Ice Tongue Inferred from Geodetic GNSS Observations and SAR Offset Tracking Analysis

David Glacier and Drygalski Ice Tongue are massive glaciers in Victoria Land, Antarctica. The ice from the East Antarctic Ice Sheet is drained through the former, and then discharged into the western Ross Sea through the latter. David Drygalski is the largest outlet glacier in Northern Victoria Land, floating kilometers out to sea. The floating and grounded part of the David Glacier are the main focus of this article. During the XXI Italian Antarctic Expedition (2005–2006), within the framework of the National Antarctic Research Programme (PNRA), two GNSS stations were installed at different points: the first close to the grounding line of David Glacier, and the second approximately 40 km downstream of the first one. Simultaneous data logging was performed by both GNSS stations for 24 days. In the latest data processing, the kinematic PPP technique was adopted to evaluate the dominant diurnal components and the very small semi-diurnal variations in ice motion induced by the ocean tide and the mean ice flow rates of both GNSS stations. Comparison of the GNSS time series with predicted ocean tide calculated from harmonic coefficients of the nearest tide gauge stations, installed at Cape Roberts and Mario Zucchelli Station, highlight different local response of the glacier to ocean tide, with a minor amplitude of vertical motion at a point partially anchored at the bedrock close to the grounding line. During low tide, the velocity of the ice flow reaches its daily maximum, in accordance with the direction of seawater outflow from the fjord into the ocean, while the greatest daily tidal excursion generates an increase in the horizontal ice flow velocity. With the aim to extend the analysis in spatial terms, five COSMO-SkyMED Stripmap scenes were processed. The comparison of the co-registered offset tracking rates, obtained from SAR images, with the GNSS estimation shows good agreement.

Mathematical Analysis of Gold, Platinum, and Magnetite Nanoparticle Shapes on Unsteady Radiative Flow of Nanoliquid along an Infinite Vertical Flat Plate in the Proximity of Convective Boundary Condition

In this article, the heat transfer and flow pattern characteristics are discussed in the proximity of convective boundary condition for three kinds of nanoparticles, namely gold, Platinum and magnetite with three different shapes, namely spherical, platelets, and lamina. Here water is taken as a base liquid. The thermal radiation impact is assumed into account. The partial differential equations are shifted into ordinary differential equations by applying an acceptable transformation and then exact solutions are acquired by promoting the Laplace transform technique. Solid volume fraction is fluctuated as 5%, 10%, 15%, and 20%. The variations of nanoliquid motion and heat transfer are displayed graphically as well as the numerical values of skin friction and rate of heat transfer at the plate are displayed in tabular pattern. In particular, the liquid motion as well as the heat transfer is least for lamina type nanoparticles, medium for platelet type nanoparticles, and greatest for spherical type nanoparticles. Moreover, the skin friction escalates and the rate of heat transfer declines for three types of nanoliquids in three distinct shapes with the progress of time. This report can be further utilized to authenticate the effectiveness of acquired mathematical results for another sophisticated nanoliquid problems.

Quantifying Velopharyngeal Motion Variation in Speech Sound Production Using an Audio-Informed Dynamic MRI Atlas.

New developments in dynamic magnetic resonance imaging (MRI) facilitate high-quality data acquisition of human velopharyngeal deformations in real-time speech. With recently established speech motion atlases, group analysis is made possible via spatially and temporally aligned datasets in the atlas space from a desired population of interest. In practice, when analyzing motion characteristics from various subjects performing a designated speech task, it is observed that different subjects' velopharyngeal deformation patterns could vary during the pronunciation of the same utterance, regardless of the spatial and temporal alignment of their MRI. Since such variation can be subtle, identification and extraction of unique patterns out of these high-dimensional datasets is a challenging task. In this work, we present a method that computes and visualizes subtle deformation variation patterns as principal components of a subject group's dynamic motion fields in the atlas space. Coupled with the real-time speech audio recordings during image acquisition, the key time frames that contain maximum speech variations are identified by the principal components of temporally aligned audio waveforms, which in turn inform the temporal location of the maximum spatial deformation variation. Henceforth, the motion fields between the key frames and the reference frame for each subject are computed and warped into the common atlas space, enabling a direct extraction of motion variation patterns via quantitative analysis. The method was evaluated on a dataset of twelve healthy subjects. Subtle velopharyngeal motion differences were visualized quantitatively to reveal pronunciation-specific patterns among different subjects.

Method for Determining Effective Frequency Range of Coherency Based on Seismic Energy

When using data recorded by dense instrument arrays to fit the coherency model of spatial variation of seismic ground motions, the selected frequency range may affect the fitting parameters, which also affects the synthesized time history of the ground motion field. In this study, the acceleration of the 5th and the 45th Earthquake of the SMART-1 array was selected. The Abrahamson and Loh models were used to perform the parameter fitting of the coherency for frequency ranges of 0–8, 0–16 and 0–24[Formula: see text]Hz, and the obtained results were different. The smaller the frequency range, the lower the fitted lagged coherency. The influence of frequency range on fitting parameters of Loh model is much greater than that of Abrahamson model. Based on the relative relationship of ground motion energy distribution in each frequency band represented by the power spectra, from the perspective of power spectrum energy, the energy ratio concept was introduced, i.e. the ratio of the power spectra for a specific frequency range to the total power spectra. Based on the energy ratio, a method for determining the effective frequency range of the coherency model was developed. Through the comparison and analysis of the horizontal-component acceleration of the SMART-1 array, it was found that when the frequency range was 0–5[Formula: see text]Hz, the energy ratio exceeded 95%, when the frequency range was 0–8[Formula: see text]Hz, the energy in the frequency band reached approximately 99% of the total energy. Therefore, it is recommended that when the parameters of the coherency model are fitted, the frequency corresponding to an energy ratio of 95% can be used as the effective frequency. Within this range, the contamination of high-frequency components on the fitting results is minimized.

Facial Micro-Expression Recognition Using Double-Stream 3D Convolutional Neural Network with Domain Adaptation.

Humans show micro-expressions (MEs) under some circumstances. MEs are a display of emotions that a human wants to conceal. The recognition of MEs has been applied in various fields. However, automatic ME recognition remains a challenging problem due to two major obstacles. As MEs are typically of short duration and low intensity, it is hard to extract discriminative features from ME videos. Moreover, it is tedious to collect ME data. Existing ME datasets usually contain insufficient video samples. In this paper, we propose a deep learning model, double-stream 3D convolutional neural network (DS-3DCNN), for recognizing MEs captured in video. The recognition framework contains two streams of 3D-CNN. The first extracts spatiotemporal features from the raw ME videos. The second extracts variations of the facial motions within the spatiotemporal domain. To facilitate feature extraction, the subtle motion embedded in a ME is amplified. To address the insufficient ME data, a macro-expression dataset is employed to expand the training sample size. Supervised domain adaptation is adopted in model training in order to bridge the difference between ME and macro-expression datasets. The DS-3DCNN model is evaluated on two publicly available ME datasets. The results show that the model outperforms various state-of-the-art models; in particular, the model outperformed the best model presented in MEGC2019 by more than 6%.

Visualization study on the flow characteristics of a nitrogen pulsating heat pipe

An experimental setup for visualization study of a nitrogen pulsating heat pipe (PHP) is developed. The PHP is a flat-plate PHP fabricated using MEMS technology with silicon base plate and BF33 glass cover plate. The hydraulic diameter of the channel is 667 μm and the overall dimension of the PHP is 70 × 25 × 2 mm3. The filling ratios (FRs) in this study are measured with visualization images. Six filling ratios are adopted ranging from 15% ∼ 94% and the PHP only fails to start-up with 15% FR. Flow images of nitrogen PHPs are obtained for the first time. Four types of flow patterns are observed. The variations of flow characteristics and fluid motion with heat loads and FRs are analyzed. The fluid motion diagram and flow pattern proportion are extracted from the visualization results as well. Large amplitude oscillating motion is rarely observed in the nitrogen PHP. And annular flow is found to be dominant in low and medium FR working conditions, which indicates the one-dimensional slug flow model may have a narrow application range for the flat-plate nitrogen PHP.

Research on temperature drift mechanism and compensation method of silicon piezoresistive pressure sensors

Silicon piezoresistive pressure sensors have played a key role in the detection field due to their small size, high sensitivity, and high integration. However, the factors such as its material particularity, the temperature dependence of the piezoresistive coefficient, the variation of carrier motion in the material caused by heat-induced stress, doping concentration, and resistance mismatch may cause the sensor to generate temperature drift. In this paper, the mechanism of temperature drift of silicon-based pressure sensor is analyzed initially, and then a comparison between BP neural network and wavelet neural network is done, which shows the root mean square error of the wavelet neural network is much smaller than that of the BP neural network and better linearity.

Evaluation of underground blast-induced ground motions through near-surface low-velocity geological layers

Surface ground motion produced by underground blasts is significantly influenced by near-surface geological conditions. However, near-surface low-propagation velocity layers were always ignored in past analyses of ground motions due to their thin thickness. With the rising concern about surface ground motions produced by the ascendant scale and frequentness of underground excavation and mining, close attention is gradually paid to ground blast vibrations. Therefore, systemic experiments were conducted and took seven months in an underground mine to clarify the variation of motion from underground rock to surface ground. The attenuation of surface ground peak particle velocities (PPVs) is compared to that in underground rock, and horizontal amplitudes are compared to vertical amplitudes. Differences between bedrock and surface ground vibrations are analyzed to illustrate the site effect of near-surface lower-propagation velocity layers. One-dimensional site response analysis is employed to quantify the influence of different geological profiles on surface ground vibrations. The experimental data and site response analysis allowed the following conclusions: (1) geological site effects mainly produce decreasing dominant frequency (DF) of surface ground vibrations; (2) the site amplification effect of blast vibration needs to be characterized by peak particle displacement (PPD); (3) shear waves (S-waves) begin to dominate and surface Rayleigh waves (R-waves) develop as blast-induced ground vibrations travel upward through rock and lower-velocity layers to the surface. The comparison of response relative displacement to a critical value is best to assess the potential for cracking on surface structures.

Dynamics of collective motion across time and species.

Most studies of collective animal behaviour rely on short-term observations, and comparisons of collective behaviour across different species and contexts are rare. We therefore have a limited understanding of intra- and interspecific variation in collective behaviour over time, which is crucial if we are to understand the ecological and evolutionary processes that shape collective behaviour. Here, we study the collective motion of four species: shoals of stickleback fish (Gasterosteus aculeatus), flocks of homing pigeons (Columba livia), a herd of goats (Capra aegagrus hircus) and a troop of chacma baboons (Papio ursinus). First, we describe how local patterns (inter-neighbour distances and positions), and group patterns (group shape, speed and polarization) during collective motion differ across each system. Based on these, we place data from each species within a 'swarm space', affording comparisons and generating predictions about the collective motion across species and contexts. We encourage researchers to add their own data to update the 'swarm space' for future comparative work. Second, we investigate intraspecific variation in collective motion over time and provide guidance for researchers on when observations made over different time scales can result in confident inferences regarding species collective motion. This article is part of a discussion meeting issue 'Collective behaviour through time'.

Determining the relationship between tibiofemoral geometry and passive motion with partial least squares regression.

Tibiofemoral geometry influences knee passive motion and understanding their relationship can provide insight into knee function and mechanisms of injury. However, the complexity of the geometric constraints has made characterizing the relationship challenging. The aim of this study was to determine the tibiofemoral bone geometries that explain the variation in passive motion using a partial least squares regression (PLSR) model. The PLSR model was developed for 29 healthy cadaver specimens (10 female, 19 male) with femur and tibia geometries retrieved from MRIimages and six degree-of-freedomtibiofemoral kinematics determined during a flexion cycle with minimal medial pressure. The first 13 partial least squares(PLS) components explained 90% of the variation in the kinematics and accounted for 89% of the variation in geometry. The first three PLS components which shared geometric changes to particular surface congruencies of the tibial and femoral condyles explained the most amount of variation in the kinematics, primarily in anterior-posterior translation. Meanwhile, variations in femoral condyle width and the intercondylar space, tibia plateau size and conformity, and tibia eminences heights in PLS2 and 4 explained the greatest amount of variation in internal-external rotation. PLS 4 exhibiting variation in overall size of the knee accounted for greatest amount of variation in geometry (50%) and had the greatest influence on the abduction-adduction motion and some on internal-external rotation but, overall, explained only a small proportion of the kinematics (10%). Elucidating the complex relationship between tibiofemoral bone geometry and passive kinematics may help personalize treatments for improved functional outcomes in patients.

Multi-level channel attention excitation network for human action recognition in videos

Channel attention mechanism has continuously attracted strong interests and shown great potential in enhancing the performance of deep CNNs. However, when applied to video-based human action recognition task, most existing methods generally learn channel attention at frame level, which ignores the temporal dependencies and may limit the recognition performance. In this paper, we propose a novel multi-level channel attention excitation (MCAE) module to model the temporal-related channel attention at both frame and video levels. Specifically, based on video convolutional feature maps, frame-level channel attention (FCA) is generated by exploring time-channel correlations, and video-level channel attention (VCA) is generated by aggregating global motion variations. MCAE firstly recalibrates video feature responses with frame-wise FCA, and then activates the motion-sensitive channel features with motion-aware VCA. MCAE module learns the channel discriminability from multiple levels and can act as a guidance to facilitate efficient spatiotemporal feature modeling in activated motion-sensitive channels. It can be flexibly embedded into 2D networks with very limited extra computation cost to construct MCAE-Net, which effectively enhances the spatiotemporal representation of 2D models for video action recognition task Extensive experiments on five human action datasets show that our method achieves superior or very competitive performance compared with the state-of-the-arts, which demonstrates the effectiveness of the proposed method for improving the performance of human action recognition.

Identification and compensation of position independent geometric errors of dual rotary axes for hybrid-type five-axis machine tool based on unit dual quaternions

This paper presents a novel synchronous motion trajectory based on the unit dual quaternion (UDQ) to identify the position-independent geometric errors (PIGEs) of the dual rotary axes of a hybrid five-axis machine tool using a Double ball-bar (DBB). A general irregular trajectory equipartition method is presented to deal with the asynchronous matching between DBB data sampling and trajectory points. The kinematic model is effectively simplified by redefining the PIGEs of linear/rotary axes. The installation errors of the DBB are obtained by circular/spherical fitting trajectories, which are brought into the error model for elimination. The simulation of the UDQ kinematic model combined with preset error value shows that the non-uniform variation of DBB motion will lead to the deviation of PIGEs decoupling results. A compensation strategy of major orientation errors based on the UDQ is proposed, which is verified by simulation and compensation experiments.

Direction-guided two-stream convolutional neural networks for skeleton-based action recognition

In skeleton-based action recognition, treating skeleton data as pseudoimages using convolutional neural networks (CNNs) has proven to be effective. However, among existing CNN-based approaches, most focus on modeling information at the joint-level ignoring the size and direction information of the skeleton edges, which play an important role in action recognition, and these approaches may not be optimal. In addition, combining the directionality of human motion to portray action motion variation information is rarely considered in existing approaches, although it is more natural and reasonable for action sequence modeling. In this work, we propose a novel direction-guided two-stream convolutional neural network for skeleton-based action recognition. In the first stream, our model focuses on our defined edge-level information (including edge and edge_motion information) with directionality in the skeleton data to explore the spatiotemporal features of the action. In the second stream, since the motion is directional, we define different skeleton edge directions and extract different motion information (including translation and rotation information) in different directions to better exploit the motion features of the action. In addition, we propose a description of human motion inscribed by a combination of translation and rotation, and explore how they are integrated. We conducted extensive experiments on two challenging datasets, the NTU-RGB+D 60 and NTU-RGB+D 120 datasets, to verify the superiority of our proposed method over state-of-the-art methods. The experimental results demonstrate that the proposed direction-guided edge-level information and motion information complement each other for better action recognition.

Dynamic cone-beam CT reconstruction using spatial and temporal implicit neural representation learning (STINR)

Objective. Dynamic cone-beam CT (CBCT) imaging is highly desired in image-guided radiation therapy to provide volumetric images with high spatial and temporal resolutions to enable applications including tumor motion tracking/prediction and intra-delivery dose calculation/accumulation. However, dynamic CBCT reconstruction is a substantially challenging spatiotemporal inverse problem, due to the extremely limited projection sample available for each CBCT reconstruction (one projection for one CBCT volume). Approach. We developed a simultaneous spatial and temporal implicit neural representation (STINR) method for dynamic CBCT reconstruction. STINR mapped the unknown image and the evolution of its motion into spatial and temporal multi-layer perceptrons (MLPs), and iteratively optimized the neuron weightings of the MLPs via acquired projections to represent the dynamic CBCT series. In addition to the MLPs, we also introduced prior knowledge, in the form of principal component analysis (PCA)-based patient-specific motion models, to reduce the complexity of the temporal mapping to address the ill-conditioned dynamic CBCT reconstruction problem. We used the extended-cardiac-torso (XCAT) phantom and a patient 4D-CBCT dataset to simulate different lung motion scenarios to evaluate STINR. The scenarios contain motion variations including motion baseline shifts, motion amplitude/frequency variations, and motion non-periodicity. The XCAT scenarios also contain inter-scan anatomical variations including tumor shrinkage and tumor position change. Main results. STINR shows consistently higher image reconstruction and motion tracking accuracy than a traditional PCA-based method and a polynomial-fitting-based neural representation method. STINR tracks the lung target to an average center-of-mass error of 1–2 mm, with corresponding relative errors of reconstructed dynamic CBCTs around 10%. Significance. STINR offers a general framework allowing accurate dynamic CBCT reconstruction for image-guided radiotherapy. It is a one-shot learning method that does not rely on pre-training and is not susceptible to generalizability issues. It also allows natural super-resolution. It can be readily applied to other imaging modalities as well.

Source parameter analysis using distributed acoustic sensing – an example with the PoroTomo array

SUMMARY In this study, I demonstrate that distributed acoustic sensing (DAS) raw strain rate data can directly be used to estimate spectral source parameters through an Empirical Green's Function (EGF) deconvolution analysis. Previously, DAS had been widely used in passive seismology to image the subsurface and analyze ground motion variations by converting strain or strain rate to particle velocity or acceleration prior to analysis. In this study, spectral analysis is applied to the PoroTomo joint DAS and seismic Nodal array in the Brady Hot Springs geothermal field to obtain source parameters for two M4 earthquakes via EGF analysis, where nearly collocated smaller events are used as an EGF to remove path and site effects. The EGF workflow is applied to raw DAS strain rate data without conversion to particle velocities and raw Nodal seismic data. The DAS and Nodal results are very consistent with similar features of spectral ratios, corner frequencies and moment ratios for the same event pairs. The uncertainty due to stacked spectral measurement is much lower on the DAS array, suggesting better stability of spectral shape measurement, possibly due to the much denser spatial sampling. The uncertainty due to model fitting is similar between DAS and Nodal arrays with slightly lower uncertainty on the DAS array. These observations demonstrate potential for directly using the strain rate measurements from DAS arrays for earthquake source characterizations.

An Integrated Multi-Factor Coupling Approach for Marine Dynamic Disaster Assessment in China’s Coastal Waters

Marine dynamic disasters, such as storm surges and huge waves, can cause large economic and human losses. The assessment of marine dynamic disasters is, thus, important, but improvements to its reliability are needed. The current study improved and integrated the assessment from the perspective of multi-factor coupling. Using a weighted index system, a marine dynamic disaster assessment indicator system suitable for China’s coastal waters was established, and a method for calculating the weight of disaster indicators was proposed from the perspective of rapid assessment. To reduce the assessment deviation in coastal waters, a multi-factor coupling algorithm was proposed. This algorithm obtained amplitude variations of wave orbital motion in horizontal and vertical directions, which was used to evaluate the influence of background current and terrain slope on coastal ocean waves. Landsat 8 remote sensing images were used to carry out an object-oriented extraction of raft and cage aquaculture areas in China’s coastal waters. The aquaculture density was then used as the main basis for a vulnerability assessment. Finally, the whole assessment system was integrated and verified during a typical storm surge process in coastal waters around the Shandong Peninsula in China. The coupled variations were also added to the assessment process and increased the risk value by an average of 12% in the High Sea States of the case study.