Intellectual Property Rights Protection and Imitation - An empirical examination of Japanese FDI in China -

By using the data obtained from a questionnaire survey to the Japanese firms in China this paper empirically examines the effects of the IPRs protection against local illegal imitation. No evidence has been found in the test that the patent and trademark registration, which constitutes a part of the whole IPRs protection system, has protective effect. To the contrary the results suggest that the patent and trademark registration system may play a role in facilitating local illegal imitation and may be mediate technology transfer/diffusion in China.IPR, Patent, Imitation, Technology diffusion, FDI

Local-global compatibility of mod pp Langlands program for certain Shimura varieties

We generalize the local-global compatibility result in arXiv:1506.04022 to higher dimensional cases, by examining the relation between Scholze's functor and cohomology of Kottwitz-Harris-Taylor type Shimura varieties. Along the way we prove a cuspidality criterion from type theory. We also deal with compatibility for torsion classes in the case of semisimple mod $p$ Galois representations with distinct irreducible components under certain flatness hypotheses.Comment: 21 pages, comments welcom

HUMAN BRAIN WHITE MATTER ANALYSIS USING TRACTOGRAPHY —AN ATLAS-BASED APPROACH

The human brain is connected via a vastly complex network of white matter fiber pathways. However, this structural connectivity information cannot be obtained from conventional MRI, in which much of white matter appears homogeneous. Diffusion tensor imaging can estimate fiber orientation by measuring the anisotropy of water diffusion. Using tractography, the brain connectivity can be studied non-invasively. Past tractography studies have shown that the cores of prominent white matter tracts can be faithfully reconstructed. Superimposing the tract coordinates on various MR images, MR metrics can be quantified in a tract-specific manner. However, tractography results are often contaminated by partial volume effect and imaging noise. Particularly, tractography often fails under white matter pathological conditions, which render tract-specific analysis impractical. In order to address these issues, we introduced an atlas-based approach. Four novel atlas-based approaches were included in this data analysis framework. First, statistical templates of major white matter tracts were created using a DTI database of normal subjects. The statistical white matter tract templates can serve two purposes. First, the statistical template can be used as a reference to detect abnormal white matter anatomy in neurodegenerative diseases. Second, the statistical template can be applied to individual patient data for automated white matter parcellation and tract-specific quantification. In the second approach, the trajectory of white matter fiber bundles was used to estimate the cortical regions associated with specific tracts of interest. Using this approach, cortical regions were reproducibly identified on the population-averaged cortical maps of brain connectivity. Third, we improved the accuracy of the population-based tract analysis by incorporating a highly elastic image transformation technique, called Large Deformation Diffeomorphic Metric Mapping (LDDMM). As a testament to the power of this algorithm, we successfully applied tract-specific analysis on Alzheimer’s patients. The last approach was to analyze the brain cortical connection networks using automatic fiber tracking. A tracking pipeline was built by combining White Matter Parcellation Map (WMPM), brute-force tractography and topology-preserving image transformation LDDMM. This novel tracking pipeline was applied on patient group with Alzheimer’s disease. The connectivity networks of Alzheimer’s patients were compared with age-matched controls using multivariate pattern classification

Experimental investigation on shock mechanical properties of red sandstone under preloaded 3D static stresses

Triaxial impact mechanical performance experiment was performed to study the mechanical properties of red sandstone subjected to three-dimensional (3D) coupled static and dynamic loads, i.e., three confining pressures (0, 5, and 10 MPa) and three axial pressures (11, 27, and 43 MPa). A modified 3D split Hopkinson pressure bar testing system was used. The change trend in the deformation of red sandstone and the strength and failure modes under axial pressures and confining pressures were analyzed. Results show that, when the confining pressure is constant, the compressive strength, secant modulus, and energy absorbed per unit volume of red sandstone initially increases and subsequently decreases, whereas the average strain rate exhibits an opposite trend. When the axial pressure is constant, both the compressive strength and secant modulus of red sandstone are enhanced, but the average strain rate is decreased with increasing confining pressure. The energy absorbed per unit volume is initially increased and subsequently decreased as the confining pressure increases. Red sandstone exhibits a cone-shaped compression–shear failure mode under the 3D coupled static and dynamic loads. The conclusions serve as theoretical basis on the mechanical properties of deep medium-strength rock under a high ground stress and external load disturbance condition

Gas Viscosity at High Pressure and High Temperature

Gas viscosity is one of the gas properties that is vital to petroleum engineering. Its role in the oil and gas production and transportation is indicated by its contribution in the resistance to the flow of a fluid both in porous media and pipes. Although viscosity of some pure components such as methane, ethane, propane, butane, nitrogen, carbon dioxide and binary mixtures of these components at low-intermediate pressure and temperature had been studied intensively and been understood thoroughly, very few investigations were performed on viscosity of naturally occurring gases, especially gas condensates at low-intermediate pressure and temperature, even fewer lab data were published. No gas viscosity data at high pressures and high temperatures (HPHT) is available. Therefore this gap in the oil industry still needs to be filled. Gas viscosity at HPHT becomes crucial to modern oil industry as exploration and production move to deep formation or deep water where HPHT is not uncommon. Therefore, any hydrocarbon encountered there is more gas than oil due to the chemical reaction causing oil to transfer to gas as temperature increases. We need gas viscosity to optimize production rate for production system, estimate reserves, model gas injection, design drilling fluid, and monitor gas movement in well control. Current gas viscosity correlations are derived using measured data at low-moderate pressures and temperatures, and then extrapolated to HPHT. No measured gas viscosities at HPHT are available so far. The validities of these correlations for gas viscosity at HPHT are doubted due to lack of experimental data. In this study, four types of viscometers are evaluated and their advantages and disadvantages are listed. The falling body viscometer is used to measure gas viscosity at a pressure range of 3000 to 25000 psi and a temperature range of 100 to 415 oF. Nitrogen viscosity is measured to take into account of the fact that the concentration of nonhydrocarbons increase drastically in HPHT reservoir. More nitrogen is found as we move to HPHT reservoirs. High concentration nitrogen in natural gas affects not only the heat value of natural gas, but also gas viscosity which is critical to petroleum engineering. Nitrogen is also one of common inject gases in gas injection projects, thus an accurate estimation of its viscosity is vital to analyze reservoir performance. Then methane viscosity is measured to honor that hydrocarbon in HPHT which is almost pure methane. From our experiments, we found that while the Lee-Gonzalez-Eakin correlation estimates gas viscosity at a low-moderate pressure and temperature accurately, it cannot give good match of gas viscosity at HPHT. Apparently, current correlations need to be modified to predict gas viscosity at HPHT. New correlations constructed for HPHT conditions based on our experiment data give more confidence on gas viscosity

Evaluation of Liquid Loading in Gas Wells Using Machine Learning

The inevitable result that gas wells witness during their life production is the liquid loading problem. The liquids that come with gas block the production tubing if the gas velocity supplied by the reservoir pressure is not enough to carry them to surface. Researchers used different theories to solve the problem naming, droplet fallback theory, liquid film reversal theory, characteristic velocity, transient simulations, and others. While there is no definitive answer on what theory is the most valid or the one that performs the best in all cases. This paper comes to involve a different approach, a combination between physics-based modeling and statistical analysis of what is known as Machine Learning (ML). The authors used a refined ML algorithm named XGBoost (extreme gradient boosting) to develop a novel full procedure on how to diagnose the well with liquid loading issues and predict the critical gas velocity at which it starts to load if not loaded already. The novel procedure includes a combination of a classification problem where a well will be evaluated based on some completion and fluid properties (diameter, liquid density, gas density, liquid viscosity, gas viscosity, angle of inclination from horizontal (alpha), superficial liquid velocity, and the interfacial tension) as a “Liquid Loaded” or “Unloaded”. The second practice is to determine the critical gas velocity, and this is done by a regression method using the same inputs. Since the procedure is a data-driven approach, a considerable amount of data (247 well and lab measurements) collected from literatures has been used. Convenient ML technics have been applied from dividing the data to scaling, modeling and assessment. The results showed that a wellconstructed XGBoost model with an optimized hyperparameters is efficient in diagnosing the wells with the correct status and in predicting the onset of liquid loading by estimating the critical gas velocity. The assessment of the model was done relatively to existing correlations in literature. In the classification problem, the model showed a better performance with an F-1 score of 0.947 (correctly classified 46 cases from 50 used for testing). In contrast, the next best model was the one by Barnea with an F-1 score of 0.81 (correctly classified 37 from 50 cases). In the regression problem, the model showed an R2 of 0.959. In contrast, the second best model was the one by Shekhar with an R2 of 0.84. The results shown here prove that the model and the procedure developed give better results in diagnosing the well correctly if properly used by engineers

Measurement method of torsional vibration signal to extract gear meshing characteristics

A technique in measuring torsional vibration signal based on an optical encoder and a discrete wavelet transform is proposed for the extraction of gear meshing characteristics. The method measures the rotation angles of the input and output shafts of a gear pair by using two optical encoders and obtains the time interval sequences of the two shafts. By spline interpolation, the time interval sequences based on uniform angle sampling can be converted into angle interval sequences on the basis of uniform time sampling. The curve of the relative displacement of the gear pair on the meshing line (initial torsional vibration signal) can then be obtained by comparing the rotation angles of the input and output shafts at the interpolated time series. The initial torsional vibration signal is often disturbed by noise. Therefore, a discrete wavelet transform is used to decompose the signal at certain scales; the torsional vibration signal of the gear can then be obtained after filtering. The proposed method was verified by simulation and experimentation, and the results showed that the method could successfully obtain the torsional vibration signal of the gear at a high frequency. The waveforms of the torsional vibration could reflect the meshing characteristics of the teeth. These findings could provide a basis for fault diagnosis of gears