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Natural gas hydrate is a kind of environmentally friendly unconventional energy with large reserves, which attract increasing attention in recent years. The microscopic pore structure and mineral composition of natural gas hydrate reservoir have a great influence on its seepage characteristics during exploitation. In this study, computed tomography and scanning electron microscope are used to obtain digital images of three clayey-silt natural gas hydrate reservoir samples in the Shenhu area of South China Sea, and then, the pore structure and the mineral composition of the samples are obtained after image processing. The result indicates that the clayey-silt samples show strong hydrophilic characteristics, small particles, good sorting properties, variable pore distribution, small average pore and throat radius, large porosity, and a large content of submicron pores. The mineral composition of the reservoir is mainly quartz and contains a certain amount of clay minerals, mainly illite, while the mineral composition of reservoir rich in microbial fossils is mainly carbonate. The results indicate that the pore structure and mineral composition of natural gas hydrate reservoirs in Shenhu area lay a foundation for the efficient development of the clayey-silt reservoir.

This paper presents a general solution for computing the multi-object posterior for sets of trajectories from a sequence of multi-object (unlabelled) filtering densities and a multi-object dynamic model. Importantly, the proposed solution opens an avenue of trajectory estimation possibilities for multi-object filters that do not explicitly estimate trajectories. In this paper, we first derive a general multi-trajectory backward smoothing equation based on random finite sets of trajectories. Then we show how to sample sets of trajectories using backward simulation for Poisson multi-Bernoulli filtering densities, and develop a tractable implementation based on ranked assignment. The performance of the resulting multi-trajectory particle smoothers is evaluated in a simulation study, and the results demonstrate that they have superior performance in comparison to several state-of-the-art multi-object filters and smoothers., Comment: Accepted for publication in IEEE Transactions on Signal Processing

Ultrasensitive gas sensors have been successfully fabricated with the high-quality COFs@SnO2@CNS heterostructures and exhibit an excellent TEA sensing performance at room temperature.

As a typical inflammatory bowel disease (IBD), ulcerative colitis (UC) becomes worldwide prevalent in recent years. Though various materials have been proved effective in reducing intestinal oxidative stress to alleviate...

This paper presents a data-driven measurement model for extended object tracking (EOT) with automotive radar. Specifically, the spatial distribution of automotive radar measurements is modeled as a hierarchical truncated Gaussian (HTG) with structural geometry parameters that can be learned from the training data. The HTG measurement model provides an adequate resemblance to the spatial distribution of real-world automotive radar measurements. Moreover, large-scale radar datasets can be leveraged to learn the geometry-related model parameters and offload the computationally demanding model parameter estimation from the state update step. The learned HTG measurement model is further incorporated into a random matrix based EOT approach with two (multi-sensor) measurement updates: one is based on a factorized Gaussian inverse-Wishart density representation and the other is based on a Rao-Blackwellized particle density representation. The effectiveness of the proposed approaches is verified on both synthetic data and real-world nuScenes dataset over 300 trajectories.

Vascular interventional surgery is the most common method for the treatment of cardiovascular diseases. Interventional surgical robot has attracted extensive attention because of its precise control and remote operation. However, conventional force sensors in surgical robots can only detect the axial thrust pressure of the catheter. Inspired by the function of insect antennae, we designed a structure with a thin-film force sensing device in the catheter head. Combined with the pressure sensor in the catheter clamping device, multiple sensor data were fused to predict and classify the current vascular environment using the LSTM network with 94.2% accuracy. During robotic surgery, real-time feedback of current pressure information and vascular curvature information can enhance doctors’ judgment of surgical status and improve surgical safety.

Evaluating the performance of multi-object tracking (MOT) methods is not straightforward, and existing performance measures fail to consider all the available uncertainty information in the MOT context. This can lead practitioners to select models which produce uncertainty estimates of lower quality, negatively impacting any downstream systems that rely on them. Additionally, most MOT performance measures have hyperparameters, which makes comparisons of different trackers less straightforward. We propose the use of the negative log-likelihood (NLL) of the multi-object posterior given the set of ground-truth objects as a performance measure. This measure takes into account all available uncertainty information in a sound mathematical manner without hyperparameters. We provide efficient algorithms for approximating the computation of the NLL for several common MOT algorithms, show that in some cases it decomposes and approximates the widely-used GOSPA metric, and provide several illustrative examples highlighting the advantages of the NLL in comparison to other MOT performance measures., Accepted to IEEE Signal Processing Letters 2021

Though fullerene has been regarded as an effective radical scavenger, the hydrophobicity strongly limits its application in biological fields. Herein, we proposed a simple photoreduction method in preparing a highly water-soluble nanocomposite consisting of ultrasmall copper nanoparticles (CuNPs) and hydroxylated fullerene (fullerenol), which showed excellent radical scavenging performance compared with traditional antioxidants. The superb antioxidative behavior was believed to be due to the synergetic effect induced by the stable decoration of CuNPs onto the surface of fullerenol, while the low metal loading level would decrease the potential toxicity of the prepared composite. Our work will offer an alternative choice for efficient antioxidative agents with more biosafety.

Although it is an essential nutrient, high choline intake directly or indirectly via its metabolite is associated with increased risk of cardiovascular disease, the mechanism of which remains to be elucidated. The present study was performed to investigate whether hydrogen sulfide (H2S) was involved in high choline-induced cardiac dysfunction and explore the potential mechanisms. We found that ejection fraction (EF) and fractional shortening (FS), the indicators of cardiac function measured by echocardiography, were significantly decreased in mice fed a diet containing 1.3% choline for 4 months as compared to the control, while applying 3,3-dimethyl-1-butanol (DMB) to suppress trimethylamine N-oxide (TMAO, a metabolite of choline) generation ameliorated the cardiac function. Subsequently, we found that feeding choline or TMAO significantly increased the protein levels of cyclic GMP-AMP (cGAMP) synthase (cGAS), stimulator of interferon genes (STING), NOD-like receptor protein 3 (NLRP3), caspase-1, and interleukin-1β (IL-1β) as compared to the control, which indicated the activation of cGAS-STING-NLRP3 inflammasome axis. Moreover, the protein expression of cystathionine γ-lyase (CSE), the main enzyme for H2S production in the cardiovascular system, was significantly increased after dietary supplementation with choline, but the plasma H2S levels were significantly decreased. To observe the effect of endogenous H2S, CSE knockout (KO) mice were used, and we found that the EF, FS, and plasma H2S levels in WT mice were significantly decreased after dietary supplementation with choline, while there was no difference between CSE KO + control and CSE KO + choline group. To observe the effect of exogenous H2S, mice were intraperitoneally injected with sodium hydrosulfide (NaHS, a H2S donor) for 4 months, and we found that NaHS improved the cardiac function and reduced the protein levels of cGAS, STING, NLRP3, caspase-1, and IL-1β in mice receiving dietary choline. In conclusion, our studies revealed that high choline diet decreased plasma H2S levels and induced cardiac dysfunction via cGAS-STING-NLRP3 inflammasome axis while H2S treatment could restore the cardiac function by inhibiting cGAS-STING-NLRP3 inflammasome axis.

Most two-dimensional (2D) materials (such as graphene and black phosphorus (BP)) exhibit the strong anisotropy in their nonlinear mechanical properties. However, it is still a tremendous challenge to modulate their anisotropy for some special applications. In this study, the mechanical anisotropy of graphene and BP can be regulated by controlling their defects (such as circular holes, elliptic holes and cracks) using molecular dynamics simulations, which is reasonable by comparison with available continuum models. For circular-hole defects, the isotropic ratios (λ) of fracture stresses and strains in both graphene and BP are lower than 0.4 when D/L ≥ 0.2 (D and L are the diameter of the circular hole and the boundary length of 2D materials, respectively.), where λ = 0 and λ = 1 represent the complete isotropy and the original anisotropy without defects, respectively. For elliptic-hole and crack defects, λ in both graphene and BP can be close to zero by controlling their sizes and oblique angles. In particular, modulating mechanical anisotropy of 2D materials is further proven by measuring the nonlinear mechanical properties of open-hole paper napkins. This study should be of great help for providing physical insights into the origins of defect-control isotropy of 2D materials.

The automotive mmWave radar plays a key role in advanced driver assistance systems (ADAS) and autonomous driving. Deep learning-based instance segmentation enables real-time object identification from the radar detection points. In the conventional training process, accurate annotation is the key. However, high-quality annotations of radar detection points are challenging to achieve due to their ambiguity and sparsity. To address this issue, we propose a contrastive learning approach for implementing radar detection points-based instance segmentation. We define the positive and negative samples according to the ground-truth label, apply the contrastive loss to train the model first, and then perform fine-tuning for the following downstream task. In addition, these two steps can be merged into one, and pseudo labels can be generated for the unlabeled data to improve the performance further. Thus, there are four different training settings for our method. Experiments show that when the ground-truth information is only available for a small proportion of the training data, our method still achieves a comparable performance to the approach trained in a supervised manner with 100% ground-truth information., Comment: Accepted by IEEE ITSC 2022

The classification of lower limb gait phase is very important for the control of exoskeleton robots. In order to enable the exoskeleton to determine gait phase and provide appropriate assistance to the wearer, we propose a compound network based on CNN-BiLSTM. The method uses data from inertial measurement units placed on the leg and pressure sensor arrays placed on the sole as inputs to the model. The convolutional neural network (CNN) is used to obtain the local key features of gait data, and then the bidirectional long short-term memory (BiLSTM) network is used to extract the serialized gait phase information from the local key features to obtain the high-level feature expression. Finally, the seven phases of both feet were obtained through the classification of the softmax layer. We designed a gait acquisition system and collected the gait data from seven subjects at varying walking speeds. In the test set, the highest gait phase classification accuracy can reach 95.09%. We compared the proposed model with the long short-term memory (LSTM) network and gated recurrent unit (GRU) network. The experimental results show that the average accuracy of CNN-BiLSTM network from seven subjects is 0.417% higher than that of the LSTM network and 0.596% higher than that of the GRU network. Therefore, the ability of the CNN-BiLSTM network to classify gait phases can be applied in designing exoskeleton controllers that can better assist for different gait phases correctly to assist the wearer to walk.

Pedestrian detection and tracking has become an important field in the field of computer vision research. However, the existing pedestrian detection algorithms have some problems, such as low accuracy and poor stability due to the similar background and overlapped occlusion interference. Therefore, an occluded pedestrian detection method based on binocular vision is proposed in this paper. We simulate the recognition of human brain and use the deep learning network MobileNet to detect and locate the initial pedestrians. Then, binocular depth is introduced as visual salience prior information, which solves the problem of identifying pedestrians with similar background and occlusion. The experimental results show that our pedestrian detection framework greatly improves the pedestrian error detection under similar background and occlusion conditions.

Tortuosity is an important parameter to characterize flow and transport in porous media. It is desired to sufficiently understand the definition of tortuosity and its calculation in different materials while the description for the travel length of the electrical/hydraulic flow in porous media is difficult in two-dimensional and three-dimensional spaces. In this chapter, we introduce the definition of tortuosity in fractal porous media, and describe its theoretical relation with length scale based on fractal geometry. The structure of a given Sierpinski carpet/sponge is used to illustrate the calculating process of tortuosity and the fractal dimension, and the numerical results show tortuosity is variable in different numerical methods. In those simulation examples of tortuosity, the logarithmic relation between tortuosity and fractal dimensions provides a framework for understanding the behavior of tortuosity in fractal porous media.

In fractal theory, the fractal dimension, lacunarity, and succolarity are usually used to characterize the structural information of the pore system. The synthetical application for the three fractal parameters is hard because of complex definitions and calculated methods. In this chapter, we mainly introduce the definitions and physical meanings of these fractal structural parameters and the calculation method based on the box-counting method from images. Furthermore, we present several examples about the application of fractal parameters in physical property modelling, natural fracture characterization, and permeability prediction. These results can well illustrate the function of the fractal dimension, lacunarity, and succolarity for the description of complexity, heterogeneity, and anisotropy of pore structures of porous media.

This paper presents a data-driven measurement model for extended object tracking (EOT) with automotive radar. Specifically, the spatial distribution of automotive radar measurements is modeled as a hierarchical truncated Gaussian with structural geometry parameters (e.g., truncation bounds, their orientation, and a scaling factor) learned from the training data. The contribution is twofold. First, the learned measurement model can provide an adequate resemblance to the spatial distribution of real-world automotive radar measurements. Second, large-scale offline training datasets can be leveraged to learn the geometry-related parameters and offload the computationally demanding model parameter estimation from the state update step. The learned structural measurement model is further incorporated into the random matrix-based EOT approach with a new state update step. The effectiveness of the proposed approach is verified on the nuScenes dataset.

This paper presents two multi-Bernoulli filters on sets of trajectories for multiple target tracking. The first filter provides a multi-Bernoulli approximation of the posterior density over the set of alive trajectories at the current time step. The second filter provides a multi-Bernoulli approximation of the posterior density over the set of all trajectories (alive and dead) up to the current time. We also explain the Gaussian implementation of the filters and compare them with other multiple target tracking algorithms in a simulated scenario.

This paper introduces the hierarchical truncated Gaussian model in representing automotive radar measurements for extended object tracking. The model aims at a flexible spatial distribution with adaptive truncation bounds to account for partial-view measurements caused by self-occlusion. Built on a random matrix approach, we propose a new state update step together with an adaptively update of the truncation bounds. This is achieved by introducing spatial-domain pseudo measurements and by aggregating partial-view measurements over consecutive time-domain scans. The effectiveness of the proposed algorithm is verified on a synthetic dataset and an independent dataset generated using the MathWorks Automated Driving toolbox.

Motivated by real-world automotive radar measurements that are distributed around object (e.g., vehicles) edges with a certain volume, a novel hierarchical truncated Gaussian measurement model is proposed to resemble the underlying spatial distribution of radar measurements. With the proposed measurement model, a modified random matrix-based extended object tracking algorithm is developed to estimate both kinematic and extent states. In particular, a new state update step and an online bound estimation step are proposed with the introduction of pseudo measurements. The effectiveness of the proposed algorithm is verified in simulations.

This paper presents a solution for recovering full trajectory information, via the calculation of the posterior of the set of trajectories, from a sequence of multitarget (unlabelled) filtering densities and the multitarget dynamic model. Importantly, the proposed solution opens an avenue of trajectory estimation possibilities for multitarget filters that do not explicitly estimate trajectories. In this paper, we first derive a general multitrajectory forward-backward smoothing equation based on sets of trajectories and the random finite set framework. Then we show how to sample sets of trajectories using backward simulation when the multitarget filtering densities are multi-Bernoulli processes. The proposed approach is demonstrated in a simulation study., Comment: Published in 23rd International Conference on Information Fusion. This arXiv version contains more detailed derivations

In this paper we introduce spatiotemporal constraints for trajectories, i.e., restrictions that the trajectory must be in some part of the state space (spatial constraint) at some point in time (temporal constraint). Spatiotemporal contraints on trajectories can be used to answer a range of important questions, including, e.g., "where did the person that were in area A at time t, go afterwards?". We discuss how multiple constraints can be combined into sets of constraints, and we then apply sets of constraints to set of trajectories densities, specifically Poisson Multi-Bernoulli Mixture (PMBM) densities. For Poisson target birth, the exact posterior density is PMBM for both point targets and extended targets. In the paper we show that if the unconstrained set of trajectories density is PMBM, then the constrained density is also PMBM. Examples of constrained trajectory densities motivate and illustrate the key results.

Robot-assisted interventional surgery can greatly reduce the radiation received by surgeons during the operation, but the lack of force detection and force feedback is still a risk in the operation which may harm the patient. In those robotic surgeries, the traditional force detection methods may have measurement losses and errors caused by mechanical transmission and cannot identify the direction of the force. In this paper, an interventional surgery robot system with a force detection device is designed and a new force detection method based on strain gauges is proposed to detect the force and infer the bending direction of the catheter in the vessel by using BP neural network. In addition, genetic algorithm is used to optimize the BP neural network, and the error between the calculated results and the actual results is reduced by 37%, which improves the accuracy of catheter bending recognition. Combining this new method with traditional force sensors not only reduces the error caused by the traditional mechanical transmission, but also can detect the bending direction of the catheter in the blood vessel, which greatly improves the safety of the operation.

Environment perception is a key enabling technology in autonomous vehicles, and multiple object tracking is an important part of this. The use of high resolution sensors, such as automotive radar and lidar, leads to the extended object tracking problem, with multiple detections per tracked object. For computationally feasible multiple extended object tracking, the data association problem must be handled. Previous work has relied on a two-step approach, using clustering algorithms, together with assignment algorithms, to achieve this. In this paper, we show that it is possible to handle the data association in a single step that works directly on the desired likelihood function. Single step data association is beneficial, because it enables better use of the measurement model and the predicted multiobject density. For single step data association, we use algorithms based on stochastic sampling, and integrate them into a Poisson Multi-Bernoulli Mixture filter. In a simulation study, and in an experiment with Velodyne data acquired in an urban environment, four sampling algorithms are compared to clustering and assignment. The results from the simulations and the experiment show that single-step likelihood-based data association achieves better performance than two-step clustering and assignment data association does.

Spontaneous imbibition widely occurs during the industrial development of tight reservoirs. Clarifying the spontaneous imbibition mechanisms and the key influencing factors contributes to the recovery of such reservoirs. In this study, basic tests of petrophysical properties and spontaneous imbibition experiments are conducted on 11 tight sandstone cores sampled from Dongying Sag, Bohai Bay Basin, China. The mass change and distribution of imbibed water are monitored by balance and nuclear magnetic resonance during the imbibition experiments. The petrophysical properties and fractal parameters are implemented to investigate the factors influencing imbibition in tight sandstone. The imbibition curves of 11 samples display a relatively similar shape but have differences in the final mass, the time required to reach the final mass and the slopes of the early stage. The effect of pore characteristics on imbibition process is explicated by nuclear magnetic resonance results. In the double logarithmic diagram, the imbibition curves can be divided into two regions. The main influencing factors of the final dimensionless imbibition mass are porosity, fractal dimension, and succolarity, indicating that the amount, complex characteristic, and connectivity of pores are critical for imbibition. However, no obvious relationship could be established between the permeability, lacunarity, and final dimensionless imbibition mass. The results assist with the clarification of the imbibition effect and its influencing factors during the industrial development of tight sandstone reservoirs.

The complex and heterogeneous microstructure of underground porous media usually results from geological processes such as deposition, compaction, cementation and fluid-mineral interactions. Variations in these microstructural characteristics largely determine the differences in the transport properties of porous media. The fractal geometry theory has been successfully applied to characterize the microstructure and transport properties of underground reservoirs. In this study, two-dimensional porous media with differently heterogeneous structures are reconstructed by utilizing the quartet structure generation set method. Fractal dimension and lacunarity are implemented to quantify the complex microstructure and analyze the relationships among these parameters. Under specific porosity, the fractal dimension increases with the reduction of lacunarity according to a power law relationship. Meanwhile, permeability has a perfect power law relationship with porosity under the same heterogeneity. The stronger heterogeneity increases the degree of pore aggregation, resulting in increased pore radius for fluid flow. For porous media with the same porosity, permeability has a significant positive power law relationship with lacunarity and a negative relationship with fractal dimension. The results demonstrate that fractal parameters are helpful to fully understand the influence of complex microstructure and chemical reactions on the macroscopic physical properties of porous media.

Fractures and fracture networks play an important role in fluid flow and transport properties of oil and gas reservoirs. Accurate estimation of geometrical characteristics of fracture networks and their hydraulic properties are two key research directions in the fields of fluids flow in fractured porous media. Recent works focusing on the geometrical, fractal and hydraulic properties of fractured reservoirs are reviewed and summarized in this mini-review. The effects of several important parameters that significantly influences hydraulic properties are specifically discussed and analyzed, including fracture length distribution, aperture distribution, boundary stress and anisotropy. The methods for predicting fractal dimension of fractures and models for fracture networks and fractured porous media based on fractal-based approaches are addressed. Some comments and suggestions are also given on the future research directions and fractal fracture networks as well as fractured porous media.

The Poisson multi-Bernoulli mixture (PMBM) is a multi-target distribution for which the prediction and update are closed. By applying the random finite set (RFS) framework to multi-target tracking with sets of trajectories as the variable of interest, the PMBM trackers can efficiently estimate the set of target trajectories. This paper derives two trajectory RFS filters for extended target tracking, called extended target PMBM trackers. Compared to the extended target PMBM filter based on sets on targets, explicit track continuity between time steps is provided in the extended target PMBM trackers., Comment: MATLAB code is available at https://github.com/yuhsuansia/Extended-Target-PMBM-Tracker. arXiv admin note: text overlap with arXiv:1812.05131

This paper presents the Gaussian implementation of the multi-Bernoulli mixture (MBM) filter. The MBM filter provides the filtering (multi-target) density for the standard dynamic and radar measurement models when the birth model is multi-Bernoulli or multi-Bernoulli mixture. Under linear/Gaussian models, the single target densities of the MBM mixture admit Gaussian closed-form expressions. Murty's algorithm is used to select the global hypotheses with highest weights. The MBM filter is compared with other algorithms in the literature via numerical simulations., Comment: Matlab code of the MBM and PMBM filters is provided in https://github.com/Agarciafernandez/MTT . Additional information on MTT including PMBM and MBM filters can be found in the online course https://www.youtube.com/channel/UCa2-fpj6AV8T6JK1uTRuFpw

A series of Tb3+ (or Bi3+) doped and Tb3+, Bi3+ co-doped CaAl2O4 phosphors were successfully prepared by the conventional solid state reaction method. The properties of the as-prepared samples, including the crystal structure, morphology, composition and photoluminescence properties are characterized by the X-ray diffraction (XRD), high-resolution transmission electron microscope (HRTEM), energy dispersive spectrometer (EDS), high-angle annular dark field (HAADF) image and spectroscopy technology, etc. The diffraction patterns of the doped samples display the peaks corresponding to the CaAl2O4 phase. The EDS and HAADF indicate that the Ca, Al, O, Tb and Bi elements are distributed uniformly in the Tb single-doped and the Tb, Bi co-doped CaAl2O4 samples. In order to investigate the energy transfer from Bi3+ to Tb3+, the critical (or optimal) doped concentrations of Tb and Bi in CaAl2O4 are obtained experimentally in sequential order. The critical doped concentrations (single-doped: 3 ​mol%Tb3+, and co-doped: 3 ​mol%Tb3+, 0.3 ​mol%Bi3+) for CaAl2O4 hosts have been determined. A strong blue-green luminescence corresponding to 5D4→7F5 transition of Tb3+ by near ultra-violet (367 ​nm) excitation and the obvious energy transfer from Bi3+ to Tb3+ in the co-doped CaAl2O4 can be observed. The energy transfer mechanism has been discussed. The CIE coordinates and decay lifetimes for the typical samples have also been investigated. It is found that CaAl2O4: Tb3+, Bi3+ is a blue-green emitting phosphor and has higher efficiency for the operation with near ultra-violet excitation.

Uniform sets of mono-crystalline nanoparticles ranging from 6 nm to over 100 nm were prepared for the MgO, Co3O4, and Fe3O4 oxide systems. The nanoparticles were characterized by transmission electron microscopy (TEM) and x-ray diffraction (XRD). A careful analysis shows increased lattice parameter for smaller nanoparticles of each oxide system: 0.47% expansion from bulk for 7 nm MgO crystallites, 0.15% expansion from bulk for 9 nm Co3O4 crystallites, and 0.13% expansion from bulk for 6 nm Fe3O4 crystallites. The compressive surface stresses and expansion energies against hydrostatic pressure for each oxide system were calculated, respectively, to be 4.13 N/m and 1.8 meV/formula unit for MgO, 3.09 N/m and 0.87 meV/formula unit for Co3O4, and 1.26 N/m and 0.67 meV/formula unit for Fe3O4. The fundamental understanding of oxide nanoparticle mechanics as presented here will facilitate integration of these materials into technological applications in a rationally designed manner.

The successful pilot testing of depressurization production for natural gas hydrate reservoir was conducted at Shenhu Area in the South China Sea. However, pressure changes due to depressurization production cause shrinkage of creeping pore and throat space for fluid flow, ultimately resulting in permeability damage in this reservoir. To optimize gas recovery for natural gas hydrate reservoir, it is important to understand the substantial variations of the pore structure and physical properties under different pressure conditions. In this study, by integrating computed tomography imaging with water flow experiments on Clay-silt sediment sample, we analyze how pore scale structural parameters (such as average pore and throat radius, pore and throat median radius, maximum pore and throat radius, and fractal dimension) change under different axial stresses by using fractal geometry approach. It is found that there is a negative relationship between axial stress and structural parameters. Meanwhile, with the axial stress increases, the range of pore and throat radius distribution located on the right of center distribution decreases. On the basis of fractal geometry theory, a fractal model is then proposed to explain the effects of axial stress and creeping microstructure on permeability for natural gas hydrate reservoir. Results show that the model provides good match with experimental data when the axial stress is larger, while poor agreements with experimental results at low pressure. This study helps us understand fundamental mechanism for permeability changes during the depressurization of gas hydrate reservoir.

The Poisson multi-Bernoulli mixture (PMBM) is an unlabelled multi-target distribution for which the prediction and update are closed. It has a Poisson birth process, and new Bernoulli components are generated on each new measurement as a part of the Bayesian measurement update. The PMBM filter is similar to the multiple hypothesis tracker (MHT), but seemingly does not provide explicit continuity between time steps. This paper considers a recently developed formulation of the multi-target tracking problem as a random finite set (RFS) of trajectories, and derives two trajectory RFS filters, called PMBM trackers. The PMBM trackers efficiently estimate the set of trajectories, and share hypothesis structure with the PMBM filter. By showing that the prediction and update in the PMBM filter can be viewed as an efficient method for calculating the time marginals of the RFS of trajectories, continuity in the same sense as MHT is established for the PMBM filter.

The tissue inhibitor of metalloproteinase (TIMP) family, including TIMP-2, regulates the activity of multifunctional metalloproteinases in pathogenesis of melanoma. The Wnt/β-catenin pathway is constitutively activated and plays a critical role in melanoma progression. However, the relationship between TIMP-2 expression and β-catenin activity is still unclear. We hypothesize that TIMP-2 over expression inhibits the activation of the Wnt/β-catenin pathway in melanoma cells. Protein expression, distribution, and transcriptional activity of β-catenin were assayed in established stable melanoma cell lines: parental A2058 expressing, A2058 T2-1 over-expressing (T2-1), and A2058 T2R-7 under-expressing (T2R-7) TIMP-2. Compared to T2-1 cells at the basal level, T2R-7 showed significantly lower amount protein and weaker immunofluorescence staining of β-catenin. This regulation is through posttranslational level via ubiquitination. Functionally, proliferation and cell growth were lower in T2R-7 compared to A2058 and T2-1. Lithium treatment was used to mimics activation of the Wnt/β-catenin pathway. In T2R-7 cells under-expressing TIMP2, lithium significantly increased total β-catenin, nuclear β-catenin, and its downstream protein phosphor-c-Myc (S62). Nuclear β-catenin staining was enhanced in T2R-7. Beta-catenin transcriptional activity and cell proliferation were also increased significantly. Axins inhibit β-catenin pathway via GSK-3 β. We further found the ratio of p-GSK-3 β (S9) to β-catenin and protein levels of Axins were significantly lower, whereas downstream Wnt 11 was high in T2R-7 treated with lithium. Collectively, the high level of TIMP-2 protein inhibits the activation of the Wnt/β-catenin pathway, thus suppressing proliferation. Insights in the molecular mechanisms of TIMP-2 may provide promising opportunities for anti-proliferative therapeutic intervention.

This paper presents a disturbing recovery approach for lower extremity exoskeletons with passive ankle joints, which particularly works when an external disturbance is applied on the standing exoskeleton. Firstly, by analyzing the mechanical structure of lower extremity exoskeletons during its stance phase, the mathematical model is established using state-space equations. Then, on the basis of LQR (Linear Quadratic Regulator) theory, a novel neural network controller is designed to obtain the optimal solution for the balance control of model proposed. Finally, experimental results demonstrate that the proposed disturbing recovery approach not only has faster recovery speed, but also can achieve higher control precision than the traditional LQR.

In this paper, we evaluate the performance of labelled and unlabelled multi-Bernoulli conjugate priors for multi-target filtering. Filters are compared in two different scenarios with performance assessed using the generalised optimal sub-pattern assignment (GOSPA) metric. The first scenario under consideration is tracking of well-spaced targets. The second scenario is more challenging and considers targets in close proximity, for which filters may suffer from coalescence. We analyse various aspects of the filters in these two scenarios. Though all filters have pros and cons, the Poisson multi-Bernoulli filters arguably provide the best overall performance concerning GOSPA and computational time.

An explicit relationship between seepage properties and microscopic structure of porous media is being sought for the development of oil and gas resources. An effective method to accurately characterize and quantify the microscopic structure of porous media is a key issue. Fractal geometry can take advantage of several physically-based parameters to analyze microstructures of reservoir rocks. In this study, three fractal structural parameters, fractal dimension, lacunarity and succolarity, were employed to characterize scale-invariant complexity, heterogeneity, and anisotropy of rock microstructures, respectively. Twelve three-dimensional digital cores of sandstone reservoir rocks were used to evaluate permeability in terms of fractal dimension, lacunarity and succolarity. The parameters were utilized to quantitatively characterize differences in core micro-structure and predict their effects on permeability. Due to the confounding influence of porosity, the fractal dimension was not an accurate predictor of the variation in permeability on its own. Instead, the results reveal that lacunarity and succolarity were better able to predict differences in structure and permeability. Succolarity, in particular, showed an exponential relationship with permeability, yielding a coefficient of determination of 0.940. Using a combination of fractal structural parameters in the place of pore-size distribution, can provide a better explanation of the relationship between fluid flow, and the heterogeneous structure and anisotropic physical properties of reservoir rocks.

Holes and defects can greatly reduce the mechanical properties of multilayer graphene sheets under different loading conditions due to the stress concentration near the hole edge in each in-plane sheet and the lack of interlayer carbon–carbon bonds between the layers. Here, we report a novel design of multilayer open-hole graphene papers (MLGPs) formed through interlayer covalent bonding at the hole edges of multilayer open-hole graphene sheets (MLGSs) under high temperature using molecular dynamics (MD) simulations. Our MD results show that the hybrid sp2–sp3 interlayer bonds of MLGPs can significantly improve their both tensile strength and interlayer shear strength. The tensile strength and interlayer shear strength of MLGPs increase by around 20% and 3 times by comparison with those of MLGSs with the same number of layers, respectively, which mainly depends on the uniformity of their interlayer bond distribution. This study can provide an effective way to improve the mechanical performances of multilayer graphene sheets with flaws and also offer corresponding guidance for the design of MLGS-based nanocomposites.

Nanoscale defects (such as cracks, holes) often occur in graphene nanoribbons (GNRs). However, it is still a big challenge to accurately predict crack-hole interactions in them. In this study, the nanocale crack-hole interactions in chiral GNRs are investigated under mode-I loading using molecular dynamics (MD) simulations and finite element (FE) analysis. The carbon-carbon (C C) bond in the FE method is modeled as a nonlinear Timoshenko beam based on the full-atom Reactive Empirical Bond-Order interatomic potential of second generation (REBO potential) for the first time. The present MD and FE results show that the shielding effects on the crack tip stress field are dominated by the angle is θ, the hole-to-crack tip spacing r and the chirality of the GNRs. Checking against the linear-elastic fracture mechanics (LEFM) predictions of some crack-hole configurations shows that the present FE method and MD simulations have high accuracy. This study should be of great help for understanding nanoscale crack-hole interactions in GNRs and providing physical insights into the origins of defect engineering of GNRs.

In this paper, nanoscale germanium (Ge) fin etching with inductively coupled plasma (ICP) equipment by Cl2/BCl3/Ar gas is experimentally demonstrated. The impact of Cl2/BCl3/Ar gas ratio on etching induced Ge surface roughness, etch rate, sidewall steepness, uniformity and layout dependence are comprehensively investigated. The surface roughness is improved by increasing Ar flow rate. A nearly vertical Ge Fin is obtained by optimizing Cl2/BCl3/Ar gas ratio. By using silicon oxide as hard mask, 60nm-width Ge Fin array with height of 100nm is experimentally illustrated with high uniformity of etch depth and Fin width. Therefore, this method shows great potential for Ge-based multi-gate device fabrication.