Vessel-targeted compensation of deformable motion in interventional cone-beam CT.

The present standard of care for unresectable liver cancer is transarterial chemoembolization (TACE), which involves using chemotherapeutic particles to selectively embolize the arteries supplying hepatic tumors. Accurate volumetric identification of intricate fine vascularity is crucial for selective embolization. Three-dimensional imaging, particularly cone-beam CT (CBCT), aids in visualization and targeting of small vessels in such highly variable anatomy, but long image acquisition time results in intra-scan patient motion, which distorts vascular structures and tissue boundaries. To improve clarity of vascular anatomy and intra-procedural utility, this work proposes a targeted motion estimation and compensation framework that removes the need for any prior information or external tracking and for user interaction. Motion estimation is performed in two stages: (i) a target identification stage that segments arteries and catheters in the projection domain using a multi-view convolutional neural network to construct a coarse 3D vascular mask; and (ii) a targeted motion estimation stage that iteratively solves for the time-varying motion field via optimization of a vessel-enhancing objective function computed over the target vascular mask. The vessel-enhancing objective is derived through eigenvalues of the local image Hessian to emphasize bright tubular structures. Motion compensation is achieved via spatial transformer operators that apply time-dependent deformations to partial angle reconstructions, allowing efficient minimization via gradient backpropagation. The framework was trained and evaluated in anatomically realistic simulated motion-corrupted CBCTs mimicking TACE of hepatic tumors, at intermediate (3.0 mm) and large (6.0 mm) motion magnitudes. Motion compensation substantially improved median vascular DICE score (from 0.30 to 0.59 for large motion), image SSIM (from 0.77 to 0.93 for large motion), and vessel sharpness (0.189 mm-1 to 0.233 mm-1 for large motion) in simulated cases. Motion compensation also demonstrated increased vessel sharpness (0.188 mm-1 before to 0.205 mm-1 after) and reconstructed vessel length (median increased from 37.37 to 41.00 mm) on a clinical interventional CBCT. The proposed anatomy-aware motion compensation framework presented a promising approach for improving the utility of CBCT for intra-procedural vascular imaging, facilitating selective embolization procedures.

Composite patch with negative Poisson's ratio mimicking cardiac mechanical properties: Design, experiment and simulation.

Developing patches that effectively merge intrinsic deformation characteristics of cardiac with superior tunable mechanical properties remains a crucial biomedical pursuit. Currently used traditional block-shaped or mesh patches, typically incorporating a positive Poisson's ratio, often fall short of matching the deformation characteristics of cardiac tissue satisfactorily, thus often diminishing their repairing capability. By introducing auxeticity into the cardiac patches, this study is trying to present a beneficial approach to address these shortcomings of the traditional patches. The patches, featuring the auxetic effect, offer unparalleled conformity to the cardiac complex mechanical challenges. Initially, scaffolds demonstrating the auxetic effect were designed by merging chiral rotation and concave angle units, followed by integrating scaffolds with a composite hydrogel through thermally triggering, ensuring excellent biocompatibility closely mirroring heart tissue. Tensile tests revealed that auxetic patches possessed superior elasticity and strain capacity exceeding cardiac tissue's physiological activity. Notably, Model III showed an equivalent modulus ratio and Poisson's ratio closely toward cardiac tissue, underscoring its outstanding mechanical potential as cardiac patches. Cyclic tensile loading tests demonstrated that Model III withstood continuous heartbeats, showcasing outstanding cyclic loading and recovery capabilities. Numerical simulations further elucidated the deformation and failure mechanisms of these patches, leading to an exploration of influence on mechanical properties with alternative design parameters, which enabled the customization of mechanical strength and Poisson's ratio. Therefore, this research presents substantial potential for designing cardiac auxetic patches that can emulate the deformation properties of cardiac tissue and possess adjustable mechanical parameters.

Hydrogels composite optimized for low resistance and loading-unloading hysteresis for flexible biosensors.

With the advancement of wearable and implantable medical devices, hydrogel flexible bioelectronic devices have attracted significant interest due to exhibiting tissue-like mechanical compliance, biocompatibility, and low electrical resistance. In this study, the development and comprehensive performance evaluation of poly(acrylic acid)/ N,N'-bis(acryloyl) cystamine/ 1-butyl-3-ethenylimidazol-1-ium:bromide (PAA/NB/IL) hydrogels designed for flexible sensor applications are introduced. Engineered through a combination of physical and chemical cross-linking strategies, these hydrogels exhibit strong mechanical properties, high biocompatibility, and effective sensing capabilities. At 95 % strain, the compressive modulus of PAA/NB/IL 100 reach up to 3.66 MPa, with the loading-unloading process showing no significant hysteresis loop, indicating strong mechanical stability and elasticity. An increase in the IL content was observed to enlarge the porosity of the hydrogels, thereby influencing their swelling behavior and sensing functionality. Biocompatibility assessments revealed that the hemolysis rate was below 5 %, ensuring their suitability for biomedical applications. Upon implantation in rats, a minimal acute inflammatory response was observed, comparable to that of the biocompatibility control poly(ethylene glycol) diacrylate (PEGDA). These results suggest that PAA/NB/IL hydrogels hold promise as biomaterials for biosensors, offering a balance of mechanical integrity, physiological compatibility, and sensing sensitivity, thereby facilitating advanced healthcare monitoring solutions.

Deformable motion compensation in interventional cone-beam CT with a context-aware learned autofocus metric.

PURPOSE: Interventional Cone-Beam CT (CBCT) offers 3D visualization of soft-tissue and vascular anatomy, enabling 3D guidance of abdominal interventions. However, its long acquisition time makes CBCT susceptible to patient motion. Image-based autofocus offers a suitable platform for compensation of deformable motion in CBCT, but it relies on handcrafted motion metrics based on first-order image properties and that lack awareness of the underlying anatomy. This work proposes a data-driven approach to motion quantification via a learned, context-aware, deformable metric, VI F D L ${\bm{VI}}{{\bm{F}}}_{DL}$ , that quantifies the amount of motion degradation as well as the realism of the structural anatomical content in the image. METHODS: The proposed VI F D L ${\bm{VI}}{{\bm{F}}}_{DL}$ was modeled as a deep convolutional neural network (CNN) trained to recreate a reference-based structural similarity metric-visual information fidelity (VIF). The deep CNN acted on motion-corrupted images, providing an estimation of the spatial VIF map that would be obtained against a motion-free reference, capturing motion distortion, and anatomic plausibility. The deep CNN featured a multi-branch architecture with a high-resolution branch for estimation of voxel-wise VIF on a small volume of interest. A second contextual, low-resolution branch provided features associated to anatomical context for disentanglement of motion effects and anatomical appearance. The deep CNN was trained on paired motion-free and motion-corrupted data obtained with a high-fidelity forward projection model for a protocol involving 120 kV and 9.90 mGy. The performance of VI F D L ${\bm{VI}}{{\bm{F}}}_{DL}$ was evaluated via metrics of correlation with ground truth VIF ${\bm{VIF}}$ and with the underlying deformable motion field in simulated data with deformable motion fields with amplitude ranging from 5 to 20 mm and frequency from 2.4 up to 4 cycles/scan. Robustness to variation in tissue contrast and noise levels was assessed in simulation studies with varying beam energy (90-120 kV) and dose (1.19-39.59 mGy). Further validation was obtained on experimental studies with a deformable phantom. Final validation was obtained via integration of VI F D L ${\bm{VI}}{{\bm{F}}}_{DL}$ on an autofocus compensation framework, applied to motion compensation on experimental datasets and evaluated via metric of spatial resolution on soft-tissue boundaries and sharpness of contrast-enhanced vascularity. RESULTS: The magnitude and spatial map of VI F D L ${\bm{VI}}{{\bm{F}}}_{DL}$ showed consistent and high correlation levels with the ground truth in both simulation and real data, yielding average normalized cross correlation (NCC) values of 0.95 and 0.88, respectively. Similarly, VI F D L ${\bm{VI}}{{\bm{F}}}_{DL}$ achieved good correlation values with the underlying motion field, with average NCC of 0.90. In experimental phantom studies, VI F D L ${\bm{VI}}{{\bm{F}}}_{DL}$ properly reflects the change in motion amplitudes and frequencies: voxel-wise averaging of the local VI F D L ${\bm{VI}}{{\bm{F}}}_{DL}$ across the full reconstructed volume yielded an average value of 0.69 for the case with mild motion (2 mm, 12 cycles/scan) and 0.29 for the case with severe motion (12 mm, 6 cycles/scan). Autofocus motion compensation using VI F D L ${\bm{VI}}{{\bm{F}}}_{DL}$ resulted in noticeable mitigation of motion artifacts and improved spatial resolution of soft tissue and high-contrast structures, resulting in reduction of edge spread function width of 8.78% and 9.20%, respectively. Motion compensation also increased the conspicuity of contrast-enhanced vascularity, reflected in an increase of 9.64% in vessel sharpness. CONCLUSION: The proposed VI F D L ${\bm{VI}}{{\bm{F}}}_{DL}$ , featuring a novel context-aware architecture, demonstrated its capacity as a reference-free surrogate of structural similarity to quantify motion-induced degradation of image quality and anatomical plausibility of image content. The validation studies showed robust performance across motion patterns, x-ray techniques, and anatomical instances. The proposed anatomy- and context-aware metric poses a powerful alternative to conventional motion estimation metrics, and a step forward for application of deep autofocus motion compensation for guidance in clinical interventional procedures.

Degradable biomedical elastomers: paving the future of tissue repair and regenerative medicine.

Degradable biomedical elastomers (DBE), characterized by controlled biodegradability, excellent biocompatibility, tailored elasticity, and favorable network design and processability, have become indispensable in tissue repair. This review critically examines the recent advances of biodegradable elastomers for tissue repair, focusing mainly on degradation mechanisms and evaluation, synthesis and crosslinking methods, microstructure design, processing techniques, and tissue repair applications. The review explores the material composition and cross-linking methods of elastomers used in tissue repair, addressing chemistry-related challenges and structural design considerations. In addition, this review focuses on the processing methods of two- and three-dimensional structures of elastomers, and systematically discusses the contribution of processing methods such as solvent casting, electrostatic spinning, and three-/four-dimensional printing of DBE. Furthermore, we describe recent advances in tissue repair using DBE, and include advances achieved in regenerating different tissues, including nerves, tendons, muscle, cardiac, and bone, highlighting their efficacy and versatility. The review concludes by discussing the current challenges in material selection, biodegradation, bioactivation, and manufacturing in tissue repair, and suggests future research directions. This concise yet comprehensive analysis aims to provide valuable insights and technical guidance for advances in DBE for tissue engineering.

Injectable Self-Expanding/Self-Propelling Hydrogel Adhesive with Procoagulant Activity and Rapid Gelation for Lethal Massive Hemorrhage Management.

Developing hydrogels that can quickly reach deep bleeding sites, adhere to wounds, and expand to stop lethal and/or noncompressible bleeding in civil and battlefield environments remains a challenge. Herein, an injectable, antibacterial, self-expanding, and self-propelling hydrogel bioadhesive with procoagulant activity and rapid gelation is reported. This hydrogel combines spontaneous gas foaming and rapid Schiff base crosslinking for lethal massive hemorrhage. Hydrogels have rapid gelation and expansion rate, high self-expanding ratio, excellent antibacterial activity, antioxidant efficiency, and tissue adhesion capacity. In addition, hydrogels have good cytocompatibility, procoagulant ability, and higher blood cell/platelet adhesion activity than commercial combat gauze and gelatin sponge. The optimized hydrogel (OD-C/QGQL-A30) exhibits better hemostatic ability than combat gauze and gelatin sponge in rat liver and femoral artery bleeding models, rabbit volumetric liver loss massive bleeding models with/without anticoagulant, and rabbit liver and kidney incision bleeding models with bleeding site not visible. Especially, OD-C/QGQL-A30 rapidly stops the bleedings from pelvic area of rabbit, and swine subclavian artery vein transection. Furthermore, OD-C/QGQL-A30 has biodegradability and biocompatibility, and accelerates Methicillin-resistant S. aureus (MRSA)-infected skin wound healing. This injectable, antibacterial, self-expanding, and self-propelling hydrogel opens up a new avenue to develop hemostats for lethal massive bleeding, abdominal organ bleeding, and bleeding from coagulation lesions.

Deformable Motion Compensation for Intraprocedural Vascular Cone-beam CT with Sequential Projection Domain Targeting and Vessel-Enhancing Autofocus.

Purpose: Cone-beam CT (CBCT) is used in interventional radiology (IR) for identification of complex vascular anatomy, difficult to visualize in 2D fluoroscopy. However, long acquisition time makes CBCT susceptible to soft-tissue deformable motion that degrades visibility of fine vessels. We propose a targeted framework to compensate for deformable intra-scan motion via learned full-sequence models for identification of vascular anatomy coupled to an autofocus function specifically tailored to vascular imaging. Methods: The vessel-targeted autofocus acts in two stages: (i) identification of vascular and catheter targets in the projection domain; and, (ii) autofocus optimization for a 4D vector field through an objective function that quantifies vascular visibility. Target identification is based on a deep learning model that operates on the complete sequence of projections, via a transformer encoder-decoder architecture that uses spatial-temporal self-attention modules to infer long-range feature correlations, enabling identification of vascular anatomy with highly variable conspicuity. The vascular autofocus function is derived through eigenvalues of the local image Hessian, which quantify the local image structure for identification of bright tubular structures. Motion compensation was achieved via spatial transformer operators that impart time dependent deformations to NPAR = 90 partial angle reconstructions, allowing for efficient minimization via gradient backpropagation. The framework was trained and evaluated in synthetic abdominal CBCTs obtained from liver MDCT volumes and including realistic models of contrast-enhanced vascularity with 15 to 30 end branches, 1 - 3.5 mm vessel diameter, and 1400 HU contrast. Results: The targeted autofocus resulted in qualitative and quantitative improvement in vascular visibility in both simulated and clinical intra-procedural CBCT. The transformer-based target identification module resulted in superior detection of target vascularity and a lower number of false positives, compared to a baseline U-Net model acting on individual projection views, reflected as a 1.97x improvement in intersection-over-union values. Motion compensation in simulated data yielded improved conspicuity of vascular anatomy, and reduced streak artifacts and blurring around vessels, as well as recovery of shape distortion. These improvements amounted to an average 147% improvement in cross correlation computed against the motion-free ground truth, relative to the un-compensated reconstruction. Conclusion: Targeted autofocus yielded improved visibility of vascular anatomy in abdominal CBCT, providing better potential for intra-procedural tracking of fine vascular anatomy in 3D images. The proposed method poses an efficient solution to motion compensation in task-specific imaging, with future application to a wider range of imaging scenarios.

Automatic Detection of Tooth-Gingiva Trim Lines on Dental Surfaces.

Detecting the tooth-gingiva trim line from a dental surface plays a critical role in dental treatment planning and aligner 3D printing. Existing methods treat this task as a segmentation problem, which is resolved with geometric deep learning based mesh segmentation techniques. However, these methods can only provide indirect results (i.e., segmented teeth) and suffer from unsatisfactory accuracy due to the incapability of making full use of high-resolution dental surfaces. To this end, we propose a two-stage geometric deep learning framework for automatically detecting tooth-gingiva trim lines from dental surfaces. Our framework consists of a trim line proposal network (TLP-Net) for predicting an initial trim line from the low-resolution dental surface as well as a trim line refinement network (TLR-Net) for refining the initial trim line with the information from the high-resolution dental surface. Specifically, our TLP-Net predicts the initial trim line by fusing the multi-scale features from a U-Net with a proposed residual multi-scale attention fusion module. Moreover, we propose feature bridge modules and a trim line loss to further improve the accuracy. The resulting trim line is then fed to our TLR-Net, which is a deep-based LDDMM model with the high-resolution dental surface as input. In addition, dense connections are incorporated into TLR-Net for improved performance. Our framework provides an automatic solution to trim line detection by making full use of raw high-resolution dental surfaces. Extensive experiments on a clinical dental surface dataset demonstrate that our TLP-Net and TLR-Net are superior trim line detection methods and outperform cutting-edge methods in both qualitative and quantitative evaluations.

Study on the influence of shot peening strengthening before shot peen forming on 2024-T351 aluminum alloy fatigue crack growth rate.

It is sparse and inconclusive that research on the subject whether the fatigue life of the structure will be reduced by shot peening strengthening before shot peen forming (S + F), and this study investigates accordingly. First, the crack growth rate test of the machine-processing plate and shot peening strengthening before shot peen forming plate demonstrate that both plates' final crack growth rate and length are similar. However, the test shows the "fluctuation phenomenon" of crack growth rate and the "intersection phenomenon" in the Paris curve. This study is based on a self-developed simulation plugin for crack growth paths. The results verify that "fluctuation" causes the differential distribution of the overall stress intensity factor in the strengthened (4.5% increase compared to machine-processing) and formed (9.8% decrease compared to machine-processing) crater areas of the shot peening strengthening before shot peen forming plate. Comparing to the full coverage strengthening area, the forming area (only 30% coverage) in the early stage of growth as well as the gain amplitude of the residual stress in the late stage of growth gradually decrease and tend to be the same as that of the machine-processing, as validated by the "intersection phenomenon".

Recent progress of antibacterial hydrogels in wound dressings.

Hydrogels are essential biomaterials due to their favorable biocompatibility, mechanical properties similar to human soft tissue extracellular matrix, and tissue repair properties. In skin wound repair, hydrogels with antibacterial functions are especially suitable for dressing applications, so novel antibacterial hydrogel wound dressings have attracted widespread attention, including the design of components, optimization of preparation methods, strategies to reduce bacterial resistance, etc. In this review, we discuss the fabrication of antibacterial hydrogel wound dressings and the challenges associated with the crosslinking methods and chemistry of the materials. We have investigated the advantages and limitations (antibacterial effects and antibacterial mechanisms) of different antibacterial components in the hydrogels to achieve good antibacterial properties, and the response of hydrogels to stimuli such as light, sound, and electricity to reduce bacterial resistance. Conclusively, we provide a systematic summary of antibacterial hydrogel wound dressings findings (crosslinking methods, antibacterial components, antibacterial methods) and an outlook on long-lasting antibacterial effects, a broader antibacterial spectrum, diversified hydrogel forms, and the future development prospects of the field.

Nanocomposite conductive tough hydrogel based on metal coordination reinforced covalent Pluronic F-127 micelle network for human motion sensing.

The design of conductive hydrogels integrating anti-fatigue, high sensitivity, strong mechanical property and good sterilization performance remains a challenge. We innovatively introduced metal coordination in covalently crosslinked Pluronic F-127 micelle network and synthesized nanocomposite conductive tough hydrogel through the combination of covalent crosslinking, metal coordination and silver nanowire reinforcement. Compared with pure diacylated PF127 hydrogel (PF127), the tensile strength of PF-AA-AM-Al3+/Ag0.25 hydrogel reaching 1.4 MPa was about 10 times than that of PF127. The toughness of PF-AA-AM-Al3+/Ag0.25 reaches 1.88 MJ/m3. Compared with PF-AA-AM-Al3+, the introduction of silver nanowires increased the fatigue life of PF-AA-AM-Al3+/Ag0.25 by 200% (31837 cycles), 170% (12804 cycles) and 1022% (511 cycles) under 100%, 120% and 150% ultimate tensile strains, respectively. Besides, the PF-AA-AM-Al3+/Ag0.25 showed strain sensitivity to small deformation (Gauge factor = 2.42) in wearable tests on hands and knees. In addition, the PF-AA-AM-Al3+/Ag0.25 had good cytocompatibility and antibacterial performance that bacteria killing ratio of 98% to S. aureus and 99% to E. coli. Finally, a viscoelastic numerical constitutive model was established based on finite element method to study the damage failure history of the material. Comparative analysis showed that local stress concentration was the main factor leading to the failure of hydrogel.

Investigation of the out-of-plane load-bearing capacity of T1100/5405 composite T-stiffened panels.

With the continuous improvement of the mechanical properties of composite materials, the adhesive interface performance of composite T-stiffened panels has become a critical factor in determining the overall structural strength. However, little work has been reported on the mechanical properties of adhesive interfaces in composite T-stiffened panels under lateral bending and shear loading. Especially, there is no clear explanation on the damage evolution law of structural properties for the interface with defects, which greatly influenced the use of T-stiffened composite structures. In this paper, the mechanical properties of T1100/5405 composite T-stiffened laminates under lateral bending and shear loading are experimentally and numerically investigated. The load-bearing capacities for the panels with intact and defected adhesive interfaces are compared, the damage evolution law of typical T-stiffened structures is further explored. Based on the continuum damage model (CDM) and the cohesive zone model (CZM), the constitutive models of the adhesive layer and the composite material are established respectively. Good agreements between experimental and numerical profiles illustrate that damages mainly occur on the loading side and the corner of the L-type ribs under lateral bending conditions, while damages extend from both sides of the interface layer to the center under shear loading. When a prefabricated defect exists, damages extend from the defect location along the loading direction. At the same time, the analysis shows that the lay-up of the surface layer, the chamfer radius, and the width of T-type ribs have a great influence on the structural load-bearing capacity, but less on the damage evolution form.

EphA2-YES1-ANXA2 pathway promotes gastric cancer progression and metastasis.

Erythropoietin-producing hepatocellular receptor A2 (EphA2) is a key member of the receptor tyrosine kinase (RTK) family, while YES Proto-Oncogene 1 (YES1) is a non-receptor tyrosine kinase (nRTK) and annexin A2 (ANXA2) belongs to the calcium-dependent phospholipid-binding protein family annexins. Here, we show that EphA2, YES1, and ANXA2 form a signal axis, in which YES1 activated by EphA2 phosphorylates ANXA2 at Tyr24 site, leading to ANXA2 activation and increased ANXA2 nuclear distribution in gastric cancer (GC) cells. Overexpression (OE) of YES1 increases, while knockdown (KD) of YES1 or ANXA2 decreases GC cell invasion and migration in vitro and tumor growth in mouse models. Reexpression of wildtype (WT) rather than mutant ANXA2 (Tyr24F) in ANXA2 knockdown (ANXA2-KD) GC cells restores YES1-induced cell invasion and migration, while neither WT nor mutant ANXA2 (Tyr24F) can restore cell invasion and migration in YES1-KD GC cells. In addition, the activation of EphA2-YES1-ANXA2 pathway is correlated with poor prognosis. Thus, our results establish EphA2-YES1-ANXA2 axis as a novel pathway that drives GC invasion and metastasis, targeting this pathway would be an efficient way for the treatment of GC.

Spider Web-Inspired Lightweight Membrane-Type Acoustic Metamaterials for Broadband Low-Frequency Sound Isolation.

Membrane-type acoustic metamaterial (MAM) has exhibited superior sound isolation properties, as well as thin and light characteristics. However, the anti-resonance modes of traditional MAMs are generated intermittently in a wide frequency range causing discontinuities in the anti-resonance modes. Achieving broadband low-frequency sound attenuation with lightweight MAM design is still a pivotal research aspect. Here, we present a strategy to realize wide sound-attenuation bands in low frequency range by introducing the design concept of bionic configuration philosophy into the MAM structures. Built by a polymeric membrane and a set of resonators, two kinds of MAM models are proposed based on the insight of a spider web topology. The sound attenuation performance and physical mechanisms are numerically and experimentally investigated. Multi-state anti-resonance modes, induced by the coupling of the bio-inspired arrangement and the host polymer film, are systematically explored. Significant sound attenuation is numerically and experimentally observed in both the lightweight bio-inspired designs. Remarkably, compared with a traditional MAM configuration, a prominent enhancement in both attenuation bandwidth and weight-reduction performance is verified. In particular, the bio-inspired MAM Model I exhibits a similar isolation performance as the reference model, but the weight is reduced by nearly half. The bio-inspired Model II broadens the sound attenuation bandwidth greatly; meanwhile, it retains a lighter weight design. The proposed bio-inspired strategies provide potential ways for designing sound isolation devices with both high functional and lightweight performance.

Mechanistic insight into methane dry reforming over cobalt: a density functional theory study.

Cobalt-based catalysts are a potential candidate among non-noble metal catalysts in dry reformation of methane (DRM), while the detailed mechanism of the DRM reaction is still largely unknown. In this contribution, the rather complicated reaction network for DRM is explored by density functional theory calculations. The most favorable adsorption structures of all species involved in the DRM reaction over Co(0001) have been identified. For CO2 activation, its direct dissociation to generate CO and O is the dominant reaction pathway. For CH4 direct dissociation, CH dehydrogenation into atomic C and H is the rate-determining step (RDS). It is predicted that the CH is the most abundant species among CHx (x = 0-3) over Co(0001). O acts as an oxidant and reacts with CH to produce CHO, and subsequently, CHO decomposes into CO and H. Atomic C may directly react with O to produce CO, or be oxidized by OH to COH, followed by the COH decomposition to CO and H. Thus, three possible pathways for DRM over the Co(0001) surface are proposed in our study, and the oxidation step is suggested as the RDS. The dominant route is identified as CH4 successive dissociation into CH, and CH oxidizing by O to form CHO, then CHO decomposition to CO and H.

Effect of intercity electric multiple unit projects on regional economic development: evidence from a natural experiment in Zhongshan city, China.

This paper analyzes the impact of the opening of an Intercity EUM (Electric Multiple Units) line, Guangzhou-Zhuhai Intercity Mass Rapid Transit, on GDP and population. By constructing the difference-in-difference (DID) model for whole 24 counties in Zhongshan of China, we found that with the opining of Intercity EMU, GDP growth and population inflow of the counties with Intercity EMU stations are significantly higher than those of without stations. Based on the Sharp Regression Discontinuity (SRD) Approach, we found that with the opining of Intercity EMU, the ratio of Guangzhou's GDP to that of other regions has obviously expanded, thus the opening of Intercity EMU has obviously widened the disparity of regional economic scale.