The Use of Value Capture for Transport Projects in China: Opportunities and Challenges

Value capture (VC) could be a useful tool to address the huge demand for public transport infrastructure funding in China. This research identifies the opportunities and challenges faced by VC implementation in China and explains how local governments and local transit agencies dealt with the regulatory barriers. The findings of this research offer insights including: (1) macro environment, regulatory framework, and supportive policy environment provide opportunities to adopt VC projects, while the risk of acquiring land vale cannot be isolated from the global political and economic situations; (2) the regulatory challenges of land transactions and lack of property tax system restrict the application of VC; (3) evidence from the case study of Shenzhen demonstrates that local government may creatively deal with the regulatory challenges to do VC and benefit local community; (4) institutional capacity is vital to implement VC. The analysis of Shenzhen experience can provide a reference for other Chinese cities to implement VC.fals

Development of superlattice CrNNbN coatings for joint replacements deposited by High Power Impulse Magnetron Sputtering

The demand for reliable coating on medical implants is ever growing. In this research, enhanced performance of medical implants was achieved by a CrN/NbN coating utilising nanoscale multilayer/superlattice structure. The advantages of the novel High Power Impulse Magnetron Sputtering technology, namely its unique highly ionised plasma were exploited to deposit dense and strongly adherent coatings on Co-Cr implants. TEM analyses revealed coating superlattice structure with bi-layer thickness of 3.5 nm. CrN/NbN deposited on Co-Cr samples showed exceptionally high adhesion, critical load values of LC2= 50 N in scratch adhesion tests. Nanoindentation tests showed high hardness of 34 GPa and Young's modulus of 447 GPa. Low coefficient of friction (µ) 0.49 and coating wear coefficient (KC) = 4.94 x 10-16 m3N-1m-1 were recorded in dry sliding tests. Metal ion release studies showed a reduction in Co, Cr and Mo release at physiological and elevated temperatures, (70 oC) to almost undetectable levels (<1 ppb). Rotating beam fatigue testing showed a significant increase in fatigue strength from 349±59 MPa (uncoated) to 539±59 MPa (coated). In vitro biological testing has been performed in order to assess the safety of the coating in biological environment, cytotoxicity, genotoxicity and sensitisation testing have been performed, all showing no adverse effects. Keywords: Orthopaedic implant, High Power Impulse Magnetron Sputtering, Superlattice coating, Corrosion, Biocompatibility

Surface-enhanced Raman spectroscopy of the endothelial cell membrane

We applied surface-enhanced Raman spectroscopy (SERS) to cationic gold-labeled endothelial cells to derive SERS-enhanced spectra of the bimolecular makeup of the plasma membrane. A two-step protocol with cationic charged gold nanoparticles followed by silver-intensification to generate silver nanoparticles on the cell surface was employed. This protocol of post-labelling silver-intensification facilitates the collection of SERS-enhanced spectra from the cell membrane without contribution from conjugated antibodies or other molecules. This approach generated a 100-fold SERS-enhancement of the spectral signal. The SERS spectra exhibited many vibrational peaks that can be assigned to components of the cell membrane. We were able to carry out spectral mapping using some of the enhanced wavenumbers. Significantly, the spectral maps suggest the distribution of some membrane components are was not evenly distributed over the cells plasma membrane. These results provide some possible evidence for the existence of lipid rafts in the plasma membrane and show that SERS has great potential for the study and characterization of cell surfaces

An Experimental Study of Low-Latency Video Streaming over 5G

Low-latency video streaming over 5G has become rapidly popular over the last few years due to its increased usage in hosting virtual events, online education, webinars, and all-hands meetings. Our work aims to address the absence of studies that reveal the real-world behavior of low-latency video streaming. To that end, we provide an experimental methodology and measurements, collected in a US metropolitan area over a commercial 5G network, that correlates application-level QoE and lower-layer metrics on the devices, such as RSRP, RSRQ, handover records, etc., under both static and mobility scenarios. We find that RAN-side information, which is readily available on every cellular device, has the potential to enhance throughput estimation modules of video streaming clients, ultimately making low-latency streaming more resilient against network perturbations and handover events.Comment: 6 Page

Domain-Independent Gesture Recognition Using Single-Channel Time-Modulated Array

In recent years, gesture recognition system based on radio frequency (RF) sensing has a wide application prospect and attraction in noncontact electronic interaction with its advantages of privacy security, lighting independence, and wide sensing range. The traditional RF sensing system depends on the environment and the subject, and the multichannel sensing equipment is expensive, which brings great challenges to the practical application. To address the above issues, a single-channel, low-cost, and domain-independent gesture recognition system is proposed. Specifically, the time-modulation technology is adopted to expand the number of antennas of the sensing device. The time-modulation array (TMA) is converted into a traditional array through harmonic recovery technology. The 2D-fast Fourier transform (FFT), moving target indication filter, and data normalization are used to extract domain-independent angle-Doppler maps (ADMs) gesture features. In order to ensure recognition accuracy, we propose a lightweight neural network with an attention mechanism, which only needs one training and can be applied to different data domains. The experimental results show that the accuracy of in-domain recognition of the proposed system is 98.9%, and the accuracy of cross-domain (i.e., new environments, new users, and new positions) recognition is 85.6%-97.4% without model retraining

Poly(Glycerol Adipate-co-ω-Pentadecalactone) Spray-Dried Microparticles as Sustained Release Carriers for Pulmonary Delivery

Purpose The aim of this work was to optimize biodegradable polyester poly(glycerol adipate-co-ω-pentadecalactone), PGA-co-PDL, microparticles as sustained release (SR) carriers for pulmonary drug delivery. Methods Microparticles were produced by spray drying directly from double emulsion with and without dispersibility enhancers ( L -arginine and L -leucine) (0.5–1.5%w/w) using sodium fluorescein (SF) as a model hydrophilic drug. Results Spray-dried microparticles without dispersibility enhancers exhibited aggregated powders leading to low fine particle fraction (%FPF) (28.79 ± 3.24), fine particle dose (FPD) (14.42 ± 1.57 μg), with a mass median aerodynamic diameter (MMAD) 2.86 ± 0.24 μm. However, L -leucine was significantly superior in enhancing the aerosolization performance ( L- arginine:%FPF 27.61 ± 4.49–26.57 ± 1.85; FPD 12.40 ± 0.99–19.54 ± 0.16 μg and MMAD 2.18 ± 0.35–2.98 ± 0.25 μm, L -leucine:%FPF 36.90 ± 3.6–43.38 ± 5.6; FPD 18.66 ± 2.90–21.58 ± 2.46 μg and MMAD 2.55 ± 0.03–3.68 ± 0.12 μm). Incorporating L -leucine (1.5%w/w) reduced the burst release (24.04 ± 3.87%) of SF compared to unmodified formulations (41.87 ± 2.46%), with both undergoing a square root of time (Higuchi’s pattern) dependent release. Comparing the toxicity profiles of PGA-co-PDL with L -leucine (1.5%w/w) (5 mg/ml) and poly(lactide-co-glycolide), (5 mg/ml) spray-dried microparticles in human bronchial epithelial 16HBE14o- cell lines, resulted in cell viability of 85.57 ± 5.44 and 60.66 ± 6.75%, respectively, after 72 h treatment. Conclusion The above data suggest that PGA-co-PDL may be a useful polymer for preparing SR microparticle carriers, together with dispersibility enhancers, for pulmonary delivery

VAMOS: a Pathfinder for the HAWC Gamma-Ray Observatory

VAMOS was a prototype detector built in 2011 at an altitude of 4100m a.s.l. in the state of Puebla, Mexico. The aim of VAMOS was to finalize the design, construction techniques and data acquisition system of the HAWC observatory. HAWC is an air-shower array currently under construction at the same site of VAMOS with the purpose to study the TeV sky. The VAMOS setup included six water Cherenkov detectors and two different data acquisition systems. It was in operation between October 2011 and May 2012 with an average live time of 30%. Besides the scientific verification purposes, the eight months of data were used to obtain the results presented in this paper: the detector response to the Forbush decrease of March 2012, and the analysis of possible emission, at energies above 30 GeV, for long gamma-ray bursts GRB111016B and GRB120328B.Comment: Accepted for pubblication in Astroparticle Physics Journal (20 pages, 10 figures). Corresponding authors: A.Marinelli and D.Zaboro

Pathogenic Mouse Hepatitis Virus or Poly(I:C) Induce IL-33 in Hepatocytes in Murine Models of Hepatitis.

International audienceThe IL-33/ST2 axis is known to be involved in liver pathologies. Although, the IL-33 levels increased in sera of viral hepatitis patients in human, the cellular sources of IL-33 in viral hepatitis remained obscure. Therefore, we aimed to investigate the expression of IL-33 in murine fulminant hepatitis induced by a Toll like receptor (TLR3) viral mimetic, poly(I:C) or by pathogenic mouse hepatitis virus (L2-MHV3). The administration of poly(I:C) plus D-galactosamine (D-GalN) in mice led to acute liver injury associated with the induction of IL-33 expression in liver sinusoidal endothelial cells (LSEC) and vascular endothelial cells (VEC), while the administration of poly(I:C) alone led to hepatocyte specific IL-33 expression in addition to vascular IL-33 expression. The hepatocyte-specific IL-33 expression was down-regulated in NK-depleted poly(I:C) treated mice suggesting a partial regulation of IL-33 by NK cells. The CD1d KO (NKT deficient) mice showed hepatoprotection against poly(I:C)-induced hepatitis in association with increased number of IL-33 expressing hepatocytes in CD1d KO mice than WT controls. These results suggest that hepatocyte-specific IL-33 expression in poly(I:C) induced liver injury was partially dependent of NK cells and with limited role of NKT cells. In parallel, the L2-MHV3 infection in mice induced fulminant hepatitis associated with up-regulated IL-33 expression as well as pro-inflammatory cytokine microenvironment in liver. The LSEC and VEC expressed inducible expression of IL-33 following L2-MHV3 infection but the hepatocyte-specific IL-33 expression was only evident between 24 to 32h of post infection. In conclusion, the alarmin cytokine IL-33 was over-expressed during fulminant hepatitis in mice with LSEC, VEC and hepatocytes as potential sources of IL-33

Data-driven prognosis method using hybrid deep recurrent neural network

Prognostics and health management (PHM) has attracted increasing attention in modern manufacturing systems to achieve accurate predictive maintenance that reduces production downtime and enhances system safety. Remaining useful life (RUL) prediction plays a crucial role in PHM by providing direct evidence for a cost-effective maintenance decision. With the advances in sensing and communication technologies, data-driven approaches have achieved remarkable progress in machine prognostics. This paper develops a novel data-driven approach to precisely estimate the remaining useful life of machines using a hybrid deep recurrent neural network (RNN). The long short-term memory (LSTM) layers and classical neural networks are combined in the deep structure to capture the temporal information from the sequential data. The sequential sensory data from multiple sensors data can be fused and directly used as input of the model. The extraction of handcrafted features that relies heavily on prior knowledge and domain expertise as required by traditional approaches is avoided. The dropout technique and decaying learning rate are adopted in the training process of the hybrid deep RNN structure to increase the learning efficiency. A comprehensive experimental study on a widely used prognosis dataset is carried out to show the outstanding effectiveness and superior performance of the proposed approach in RUL prediction