Li Model Research Articles

Practical method for studying the dispersion behavior of TiC nanoparticles in molten Mg

The dispersion behavior of TiC nanoparticles in molten Mg was investigated by using molecular dynamics simulations based on Li's theoretical model of nanoparticle capture. First, the contact angles between molten Mg and the TiC substrate were calculated over a temperature range of 973–1173 K, and the error was within 10% of the experimental values. The calculated contact angles and Li's theory, confirmed that the dispersion behavior of TiC nanoparticles in molten Mg can be of three types. Subsequently, the effects of the nanoparticle radius and temperature on the dispersion behavior of TiC nanoparticles in molten Mg were studied. The results showed that TiC nanoparticles having a radius of less than 55.8 nm can be uniformly dispersed in molten Mg at 973 K. For TiC nanoparticles having a mean particle radius of 60 nm, the temperature must exceed 1045 K to achieve self-dispersion of the nanoparticles in the Mg melt. This study presents a practical computational technique for the preparation of Mg matrix nanocomposites.

Pixelwise Gradient Model with GAN for Virtual Contrast Enhancement in MRI Imaging.

Background: The development of advanced computational models for medical imaging is crucial for improving diagnostic accuracy in healthcare. This paper introduces a novel approach for virtual contrast enhancement (VCE) in magnetic resonance imaging (MRI), particularly focusing on nasopharyngeal cancer (NPC). Methods: The proposed model, Pixelwise Gradient Model with GAN for Virtual Contrast Enhancement (PGMGVCE), makes use of pixelwise gradient methods with Generative Adversarial Networks (GANs) to enhance T1-weighted (T1-w) and T2-weighted (T2-w) MRI images. This approach combines the benefits of both modalities to simulate the effects of gadolinium-based contrast agents, thereby reducing associated risks. Various modifications of PGMGVCE, including changing hyperparameters, using normalization methods (z-score, Sigmoid and Tanh) and training the model with T1-w or T2-w images only, were tested to optimize the model's performance. Results: PGMGVCE demonstrated a similar accuracy to the existing model in terms of mean absolute error (MAE) (8.56 ± 0.45 for Li's model; 8.72 ± 0.48 for PGMGVCE), mean square error (MSE) (12.43 ± 0.67 for Li's model; 12.81 ± 0.73 for PGMGVCE) and structural similarity index (SSIM) (0.71 ± 0.08 for Li's model; 0.73 ± 0.12 for PGMGVCE). However, it showed improvements in texture representation, as indicated by total mean square variation per mean intensity (TMSVPMI) (0.124 ± 0.022 for ground truth; 0.079 ± 0.024 for Li's model; 0.120 ± 0.027 for PGMGVCE), total absolute variation per mean intensity (TAVPMI) (0.159 ± 0.031 for ground truth; 0.100 ± 0.032 for Li's model; 0.153 ± 0.029 for PGMGVCE), Tenengrad function per mean intensity (TFPMI) (1.222 ± 0.241 for ground truth; 0.981 ± 0.213 for Li's model; 1.194 ± 0.223 for PGMGVCE) and variance function per mean intensity (VFPMI) (0.0811 ± 0.005 for ground truth; 0.0667 ± 0.006 for Li's model; 0.0761 ± 0.006 for PGMGVCE). Conclusions: PGMGVCE presents an innovative and safe approach to VCE in MRI, demonstrating the power of deep learning in enhancing medical imaging. This model paves the way for more accurate and risk-free diagnostic tools in medical imaging.

A theoretical and experimental study of 2-ethylfuran + OH reaction

2-Ethylfuran (EF2), having similar thermophysical properties as gasoline, can be produced from biomass and has received increasing attention in recent years. The reaction of EF2 with OH radical plays a fundamental role in combustion chemistry of furan fuels. This work provides a comprehensive investigation on the reaction kinetics of the title reaction. The performance of several density functional theory methods is evaluated against the DLPNOCCSD(T)/CBS(T-Q) benchmark method. The results show that M08-HX/jun-cc-pVTZ is the best one for both abstraction and addition reactions with the lowest average unsigned deviations of 0.61 and 0.63 kcal mol−1, respectively. Multi-structural variational transition state theory combined with small-curvature tunneling approximation (MS-CVT/SCT) is employed to calculate the high-pressure limit (HPL) rate constants over a wide temperature range of 500−2000 K. The system-specific quantum Rice-Ramsperger-Kassel (SS-QRRK) method is used to obtain the pressure-dependent rate constants for addition reactions at several selected pressures from 0.01 to 100 atm. We have measured the rate constants of EF2 + OH reaction behind reflected shock wave at temperatures of 985−1342 K and pressure around 1 bar. The measured rate constants exhibit a positive temperature dependence and may be described by a three-parameter Arrhenius expression: k=1.28×10−10×T6.89exp(5318T) (cm3 mol−1 s–1). The calculated results are in good agreement with measurements and the total rate constants exhibit a non-monotonic temperature dependence due to the competition between the abstraction and addition channels. At the HPL, the addition reaction is dominant, and the branching ratio is larger than abstraction reaction at 500–1800 K and is 0.92 at 500 K. Finally, to evaluate the performance of our calculations on the ignition delay time of EF2, three available kinetic models (Somers et al., Xu et al. and Li et al.) of EF2 are updated and the results show that the predictions of Li's model are improved.

#5147 PREDICTING NON-DIABETIC KIDNEY DISEASE IN PATIENTS WITH TYPE 2 DIABETES MELLITUS: EXTERNAL VALIDATION OF FOUR PREDICTIVE MODELS

Abstract Background and Aims Several prediction models have been published for estimating the probability of non-diabetic kidney disease in kidney biopsies performed in patients with diabetes mellitus. External validation and comparison of these models is essential prior to their clinical application. The aim of this study was to validate and compare the predictive capacity of four previously described clinical models as a tool to predict non-diabetic kidney disease (NDKD) in diabetic patients. Method We retrospectively reviewed kidney biopsies performed in patients with type 2 diabetes mellitus between January 1999 and December 2022 in our centre. The probability of presence of NDKD was calculated by using the clinical and laboratory parameters at the time of the decision to perform a biopsy, according to 4 models; Surinrat's model, Liu's model, Li's model and Garcia-Martin's model (see Table 1). Overall model fit was assessed, calibration curves were plotted and discrimination for each model was assessed by using the receiver operating characteristic (ROC) curve in line with the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) guidelines. Results The study included 280 patients, 68.9% were males and the mean age was 65.4 ± 11.9 years. 172 patients (61.4%) had a diagnosis of non-diabetic kidney disease. On calculating the risk of NDKD, Surinrat's, Li's and Garcia-Martin's models predicted 33.6%, 64.8% and 53.4% of NDKD when using the “probable NDKD” threshold, and 60.7%, 81% and 90.7% respectively when including the “grey zones”. Fleiss’ Kappa test showed a fair agreement between the four models when using the “probable NDKD” threshold (k = 0.23, 95% CI 0.13-0.33, p<0.001) and when including the “grey zones” (k = 0.39, 95% CI 0.29-0.49, p<0.001). On using the “probable NDKD” threshold, the overall accuracy for Surinrat's, Liu's, Li's and Garcia-Martin's models was 50.7%, 62.7%, 63.6% and 65.3% respectively, while on including the “grey zones” the accuracy slightly increased in the first to models to 54.3% and 66.4% respectively while decreasing in Garcia-Martin's model to 64.2%. Area under the ROC curve (AUC) was higher in Garcia-Martin's model (AUC 0.66, 95% CI 0.58-0.74, p<0.001) compared to Lin's model (AUC 0.59, 95% CI 0.50-0.67, p = 0.044), while Surinrat's and Liu's models did not discriminate (AUC 0.55, 95% CI 0.47-0.64, p = 0.214 and AUC 0.58, 95% CI 0.49-0.63, p = 0.084). Conclusion There is a fair agreement between the four models for predicting non-diabetic kidney disease in type 2 diabetic patients. Of the models studied, Garcia-Martin's model most accurately predicted non-diabetic kidney disease in type 2 diabetes patients, although discrimination remained poor. These models may not be suitable for guiding clinicians on indicating a kidney biopsy in diabetic patients in our population.

Study on the variation rule of smoke back‐layering length of tunnel ceiling jet induced by strong fire plume

AbstractTunnel fire is a major research topic in tunnel safety. Because of the tunnel's narrow and enclosed structure, smoke movement plays an important role in the investigation of the tunnel fire. In this paper, the Froude similarity principle is used to study the variation rule of the smoke back‐layering length of tunnel ceiling jet induced by strong fire plume with different heat release rates, longitudinal ventilation velocities, and effective heights of fire source analyzed. It is found that the smoke back‐layering length of tunnel ceiling jet induced by strong fire plume increases with the increase of effective height of fire source and decreases with the increase of longitudinal ventilation velocity. When the heat release rate is relatively small (), the smoke back‐layering length of the tunnel ceiling jet induced by strong fire plume increases as the heat release rate increases. However, for the larger heat release rate (), the smoke back‐layering length of the tunnel ceiling jet induced by strong fire plume is mainly related to the longitudinal ventilation velocity. Based on Thomas's model and Li's model, by introducing the correction coefficient, correction formulas of the smoke back‐layering length of tunnel ceiling jet induced by strong fire plume changing with the effective height of fire source are proposed.

Investigation of the maximum tunnel ceiling temperature caused by two fires

This paper investigated the maximum ceiling temperature and equivalent single fire source induced by two fires combined with small-scale experimental data and numerical simulations. The influences of fire size and spacing on the maximum temperature expressed by the heat release rate (HRR) for two fires were considered. The results demonstrate that the experimental maximum temperatures in the tunnel are smaller than those obtained by Li's model. Accordingly, a model to describe the maximum temperature for two fires expressed by the HRR was proposed, and the change trend of the HRR ratio was analyzed. The predicted values agree well with the experimental and numerical values, verifying that the proposed model is reliable.

Theoretical characterization of the temperature‐dependent ultimate tensile strength of short‐fiber‐reinforced polymer composites

AbstractShort fiber/polymer composites (SFPCs) are increasingly applied in the field of high‐temperature structures. Its reliability at high temperatures has always been the core issue concerned by researchers. Here, utilizing the Force–Heat Equivalence Energy Density Principle, we developed a physically based model of temperature‐dependent ultimate tensile strength (TDUTS) for SFPCs. This model quantitatively considers the evolution of residual thermal stress, interface, fiber, and matrix performance as temperature increases. Meanwhile, the influence of fiber agglomeration frequently observed in composites and fiber orientation and length distribution is taken into account in the proposed model. The model predictions over a wide temperature range are validated against the experimental data available from the literature, indicating its reasonability and accuracy. The comparison with Kelly–Tyson model and Li's model is also performed. Additionally, the quantitative effects of the fiber agglomeration and interfacial shear strength on the TDUTS are discussed in detail. The present research not only provides an accurate and feasible approach to estimate the TDUTS of SFPCs, but also offers some helpful insights for the fabrication and design of such composites.

Pore structure, fractal characteristics and permeability prediction of tight sandstones: A case study from Yanchang Formation, Ordos Basin, China

Many fractal models have been proposed based on mercury intrusion porosimetry (MIP) for investigating the fractal property of porous materials. However, results calculated from different fractal models may be not always consistent according to previous works. In view of this, a combination of techniques including routine core analysis, MIP analysis and images analysis (thin sections and BSE) was used to study the pore structure and petrophysical properties of tight sandstones. The results of pore structure and petrophysical properties were used to check whether the fractal dimension values from five popular MIP fractal models can represent the heterogeneity of tight sandstones.It turned out that different MIP fractal models usually give different results. A single fractal dimension value representative of the entire range of pore sizes can be obtained by either Zhang and Li's model or Shen and Li's model. As fractal dimension increases, pore structure becomes more complex and permeability decreases. However, permeability or pore structure shows no significant correlation with fractal dimensions obtained from other models (Neimark's, Li's and Friensen and Mikula's model) by zone analysis. Nevertheless, two fractal regions can be identified by Neimark's or Li's model and clear inflection points could be identified on the fractal curves. The inflection points are found to be almost equal to the apex determined by Pittman's method, and could be regarded as good indicators for permeability prediction. The results obtained from Friensen and Mikula's model is more complex, and not very satisfactory to describe the heterogeneity for tight sandstones.

Understanding Eye Movements: Psychophysics and a Model of Primary Visual Cortex

Humans move their eyes in order to learn visual representations of the world. These eye movements depend on distinct factors, either by the scene that we perceive or by our own decisions. To select what is relevant to attend is part of our survival mechanisms and the way we build reality, as we constantly react both consciously and unconsciously to all the stimuli that is projected into our eyes. In this thesis we try to explain (1) how we move our eyes, (2) how to build machines that understand visual information and deploy eye movements, and (3) how to make these machines understand tasks in order to decide for eye movements.(1) We provided the analysis of eye movement behavior elicited by low-level feature distinctiveness with a dataset of 230 synthetically-generated image patterns. A total of 15 types of stimuli has been generated (e.g. orientation, brightness, color, size, etc.), with 7 feature contrasts for each feature category. Eye-tracking data was collected from 34 participants during the viewing of the dataset, using Free-Viewing and Visual Search task instructions. Results showed that saliency is predominantly and distinctively influenced by: 1. feature type, 2. feature contrast, 3. temporality of fixations, 4. task difficulty and 5. center bias. From such dataset (SID4VAM), we have computed a benchmark of saliency models by testing performance using psychophysical patterns. Model performance has been evaluated considering model inspiration and consistency with human psychophysics. Our study reveals that state-of-the-art Deep Learning saliency models do not perform well with synthetic pattern images, instead, models with Spectral/Fourier inspiration outperform others in saliency metrics and are more consistent with human psychophysical experimentation.(2) Computations in the primary visual cortex (area V1 or striate cortex) have long been hypothesized to be responsible, among several visual processing mechanisms, of bottom-up visual attention (also named saliency). In order to validate this hypothesis, images from eye tracking datasets have been processed with a biologically plausible model of V1 (named Neurodynamic Saliency Wavelet Model or NSWAM). Following Li's neurodynamic model, we define V1's lateral connections with a network of firing rate neurons, sensitive to visual features such as brightness, color, orientation and scale. Early subcortical processes (i.e. retinal and thalamic) are functionally simulated. The resulting saliency maps are generated from the model output, representing the neuronal activity of V1 projections towards brain areas involved in eye movement control. We want to pinpoint that our unified computational architecture is able to reproduce several visual processes (i.e. brightness, chromatic induction and visual discomfort) without applying any type of training or optimization and keeping the same parametrization. The model has been extended (NSWAM-CM) with an implementation of the cortical magnification function to define the retinotopical projections towards V1, processing neuronal activity for each distinct view during scene observation. Novel computational definitions of top-down inhibition (in terms of inhibition of return and selection mechanisms), are also proposed to predict attention in Free-Viewing and Visual Search conditions. Results show that our model outpeforms other biologically-inpired models of saliency prediction as well as to predict visual saccade sequences, specifically for nature and synthetic images. We also show how temporal and spatial characteristics of inhibition of return can improve prediction of saccades, as well as how distinct search strategies (in terms of feature-selective or category-specific inhibition) predict attention at distinct image contexts.(3) Although previous scanpath models have been able to efficiently predict saccades during Free-Viewing, it is well known that stimulus and task instructions can strongly affect eye movement patterns. In particular, task priming has been shown to be crucial to the deployment of eye movements, involving interactions between brain areas related to goal-directed behavior, working and long-term memory in combination with stimulus-driven eye movement neuronal correlates. In our latest study we proposed an extension of the Selective Tuning Attentive Reference Fixation Controller Model based on task demands (STAR-FCT), describing novel computational definitions of Long-Term Memory, Visual Task Executive and Task Working Memory. With these modules we are able to use textual instructions in order to guide the model to attend to specific categories of objects and/or places in the scene. We have designed our memory model by processing a visual hierarchy of low- and high-level features. The relationship between the executive task instructions and the memory representations has been specified using a tree of semantic similarities between the learned features and the object category labels. Results reveal that by using this model, the resulting object localization maps and predicted saccades have a higher probability to fall inside the salient regions depending on the distinct task instructions compared to saliency.

A new model for the transport of gaseous hydrocarbon in shale nanopores coupling real gas effect, adsorption, and multiphase pore fluid occupancies

A new gaseous hydrocarbon transport model, considering pore geometry, multiphase pore fluid occupancy, and porous deformation, has been developed to characterize the real gas flow in both nanoscale organic and inorganic porous media. To do so, Navier-Stokes equations were solved with modified second-order slip boundary conditions in nanocapillaries of organic matter (OM) and nanoslits of inorganic matrix (iOM). Due to gas adsorption, both surface diffusion and bulk gas flow affect the apparent gas permeability (AGP) of OM. Particularly, the Langmuir-slip model is used for the calculation of boundary velocity of bulk gas. In contrast, gas flow in a single nanoslit of iOM accounts for the impact of adsorbed water film with a certain thickness quantified by Li's model [15]. Additionally, porous deformation and real gas effect are included in both models of OM and iOM. As such, a unified AGP model of shale matrix is formulated based on the total organic carbon (TOC) content including contributions of both OM and iOM. Results show that, under depressurization condition, the AGP of OM/iOM with a large pore size distribution (PSD) presents a similar shape of “V”, while the AGP of iOM with a small PSD increases monotonously. The total flow capacity of OM is contributed by competitive mechanisms of surface diffusion, induced- and pure- bulk gas flow under various PSDs and pressure. For gas flow capacity in iOM with a small PSD, it can be weakened by adsorbed water film but compensated by nanoscale effect considerably at low pressures. Moreover, a higher relative humidity (RH) leads to a thicker water film resulting in a lower gas conductance. The AGP of shale matrix can be reduced or enhanced by the TOC content when accounting for gas adsorption and adsorbed water film. Besides, the real gas effect enhances flow capacity significantly in small pores with a high pressure. The proposed model provides a comprehensive understanding of the gas flow mechanism in shale nanopores under reservoir conditions.

The face-in-the-crowd effect: Threat detection versus iso-feature suppression and collinear facilitation.

Are people biologically prepared for the rapid detection of threat posed by an angry facial expression, even when it is conveyed in the form of a schematic line drawing? Based on visual search times, the current literature would suggest that the answer is yes. But are there low-level explanations for this effect? Here, we present visual search results for schematic faces using current best practice, based on a concentric search array and set size manipulation. Using this approach, we replicate the classic search advantage for angry over happy faces. However, we also report a comparable effect when abstract plus- and square-shaped stimuli-derived from the angry and happy schematic faces respectively-are used within the same paradigm. We then go on to demonstrate that, while reduced, the effect remains after removal of the circular surround, bringing us closer to the source of the effect. We explore the possibility that the source of this search asymmetry could be the iso-feature suppression and collinear facilitation model proposed in Li's (1999a, 1999b, and 2002) bottom-up model of saliency. Simulations with this model using the abstract stimuli align with the corresponding behavioral results (i.e., the plus shape was found to be more salient than the square). Given the deliberate similarities between these abstract shapes and the respective face stimuli, we propose that the underlying cause for the asymmetries typically found using schematic faces, may be more related to early visual processing of line orientation than threat detection.

A Note on the Robustness of Diebold and Li's Forecasting Results

The paper by Diebold and Li (2006) has become a benchmark in the yield curve forecasting literature, mostly owing to its excellent out-of-sample results. In this note we investigate the robustness of these outcomes in two different ways: (i) in terms of the arbitrary choices in their forecasting experimental design, and (ii) in updated samples up to 2015. In both cases, the fragility of their out-of-sample results became evident. In addition, we propose a novel decomposition method, which reveals that their positive results are unlikely to be caused by any intentionally built feature within their model. Overall, the evidence suggests that the forecasting ability of Dielbold and Li's model is questionable.

Study on the rock-electric and the relative permeability characteristics in porous rocks based on the curved cylinder-sphere model

Usually, in conventional reservoirs, the rock-electric properties can be explained by Archie's law, and the relative permeability is only considered as a function of fluid saturation. However, as for the complex reservoirs, Archie's law fails to accurately describe the rock-electric properties and the relative permeability is not only affected by saturation, the reason is that both rock-electric and relative permeability characteristics are influenced by multi-factors, including the complex pore structure, fluid distribution, and wettability. Currently, there lacks the theoretical method to comprehensively study the multi-factors effect on rock-electric and relative permeability characteristics. In this paper, the curved cylinder-sphere model is developed to illustrate the complex pore-throat structure, fluid distribution, and wettability in real rocks, which can be characterized by two important parameters: the ratio Cd of the curved cylinder radius to the sphere radius and the tortuosity τ. Based on the curved cylinder-sphere model, and by using Ohm's law, one can carry out the research on the effect of pore geometry, fluid distribution, and wettability on rock-electric characteristics. Moreover, by combining the curved cylinder-sphere model with Li's model, the effect of pore structure, fluid distribution, and wettability can be incorporated into the relative permeability calculation model. By means of the numerical simulation and analysis, it comes to conclusions that pore structure, fluid distribution, and wettability are the principal factors affecting both rock-electric and relative permeability characteristics, which may cause different formation factor, resistivity index, and relative permeability even in those reservoirs with the same porosity and fluid saturation. Besides, the study demonstrates that the complex pore structure, fluid distribution, and wettability may cause the non-Archie phenomenon of rock-electric characteristics, in addition, the complex pore structure can lead to the decrease of water relative permeability and the increase of oil relative permeability, and when the rock wettability changes from water-wet to oil-wet, water relative permeability increases and oil relative permeability decreases. Furthermore, by the comparison of the simulation results and Lab data, the important effect of complex pore structure and fluid saturation is confirmed.

Response to Comment on "Permanent human occupation of the central Tibetan Plateau in the early Holocene".

We show that Zhang and Li's sedimentological model for the Chusang travertine neglects the three-dimensional information from multiple outcrops and that their optically stimulated luminescence (OSL) age of about 20,000 years for the human imprints is untenable. We highlight the robustness of our chronology and explore reasons why Zhang and Li's OSL age is a gross overestimation of the real depositional age of the imprinted travertine.

Optimization of methods for liquid loading prediction in deep condensate gas wells

Liquid loading is a serious problem in hydrocarbon production from gas wells in the Yakela-Dalaoba deep condensate gas reservoir. The applicability of frequently-used methods for predicting liquid loading still needs to be demonstrated. For this purpose, the applicabilities of critical flow rate methods and the kinetic energy factor method are evaluated and optimized. The fundamental principles of each method for predicting liquid loading and differences among them are discussed. Different distributions of the critical flow rate and surface tension along the wellbore are considered in order to improve original critical flow rate models. For the Yakela-Dalaoba gas reservoir, the modified pseudo single-phase coupling model used for calculating the pressure and temperature distribution in high gas-liquid ratio wellbores is established, and methods suitable for calculating the pressure and temperature distribution in low gas-liquid ratio wellbores are optimized through error analysis. Results show that the modified pseudo single-phase coupling model is suitable for calculating the pressure and temperature distribution in high gas-liquid ratio wells, while Hagedorn-Brown's method and Hasan's method are suitable for low gas-liquid ratio wells. The modified critical flow rate models increase precision of liquid loading prediction compared to original models. The modified Li's model and the kinetic energy factor method which sets threshold value as 6 are of highest precision. Both of them are suitable for predicting liquid loading of gas wells in the Yakela-Dalaoba deep condensate gas reservoir.

Constitutive Model for a Confined Concrete Cylinder with an Unbonded External Steel Jacket

Early investigations focused mainly on manipulating the confinement effect to develop a reinforced concrete column with lateral hoops. Based on this legacy model, Li's model incorporated the additional confinement effect of a steel jacket. However, recent experiments on plain concrete cylinders with steel jackets revealed relatively large discrepancies in the estimates of strength enhancement and the post-peak behavior. Here, we describe a modified constitutive law for confined concrete with an unbonded external steel jacket in terms of three regions for the loading stage. We used a two-phase heterogeneous concrete model to simulate the uniaxial compression test of a <TEX>$150mm{\times}300mm$</TEX> concrete cylinder with three thicknesses of steel jackets: 1.0 mm, 1.5 mm, and 2.0 mm. The proposed constitutive model was verified by a series of finite element analyses using a finite element program. The damaged plasticity model and extended Drucker-Prager model were applied and compared in terms of the level of pressure sensitivity for confinement in 3D. The proposed model yielded results that were in close agreement with the experimental results.

A New Model for the Accurate Prediction of Liquid Loading in Low-Pressure Gas Wells

Summary The critical gas velocity and flow rate for unloading liquids from a gas well has been the subject of much interest, especially in old gas-producing fields with declining reservoir pressures. For low-pressure gas wells, Turner's model (also called Coleman's model) is judged as more suitable for predicting liquid loading in gas wells. However, field practice proves that there are still a number of low-pressure gas wells producing without loadup when the production rate is lower than Turner's minimum production rate. On the basis of experimental results, a new approach for calculating the critical gas-flow rate is introduced in this paper, which adopts Li's basic concepts, while taking into account the impact of the changes of gas-lifting efficiency caused by the rollover of droplets in the process of rising. A dimensionless parameter, loss factor S, is introduced in the new model to characterize the extent of the loss of gas energy. Well data from Coleman's paper (Coleman et al. 1991) were used in this paper for validation of the new model. The predicted results from the new model are better than those from Li's model, and even better than Turner's model. The new model is simple and can be evaluated at the wellhead when the pressure is less than 500 psia and the liquid/gas ratios range from 1 to 130 bbl/MMscf, which is suggested by Turner et al. (1969) to ensure a mist flow in gas wells.

GLOBAL AND LOCAL EFFECTS OF ROTATION: OBSERVATIONAL ASPECTS

In this paper we discussed the observational aspects of rotation in the Universe on different scales. We show dependence between the angular momentum of the structures and their size. The presented observational situation is that the galaxies, their pairs and compact groups have a non-vanishing angular momentum. In the structures of mass corresponding to groups of galaxies, this feature has not been found, while in the clusters and superclusters, alignment of galaxy orientation has been actually found. Also we know that galaxies have net angular momentum due to the fact that we actually measure the rotation curves of galaxies. These facts lead to the conclusion that theories which connect galaxy angular momentum with its surrounding structure are at some extend favored by data. We show that in the light of scenarios of galaxy formations this result could be interpreted as an effect of tidal force's mechanism, but it is also consistent with Li's model, in which galaxies form in the rotating universe. The theoretical and observational aspects of possible global rotation of the Universe were discussed as well.

A new virtual sphere method for estimating the role of thermal mass in natural ventilated buildings

The role of thermal mass is very important in natural ventilated buildings. Thermal mass can be classified as external thermal mass and internal thermal mass. In this paper, a heat balance model for naturally ventilated building is developed. The effect of external thermal mass is introduced by harmonic response method, and the effect of internal thermal mass is calculated by virtual sphere method which can lump different shapes and types into one virtual sphere. The effective convection heat transfer coefficient is presented to represents the effect of uneven distribution of internal thermal mass temperature. Through comparison with Li's model [1] and numerical calculation, it was found that the model is more suitable than the others, especially in estimating decrement factor of indoor air temperature. The errors of time lag become smaller when the fluctuation of indoor air temperature is smaller. Based on the model, a simple tool is developed to evaluate the indoor air temperature and required internal thermal mass for certain naturally ventilated buildings.

"Option Value of Harvesting" Revisited

Abstract This article corrects an internal inconsistency in Li's (1998) model of the option value of fishery harvesting. In that model, the harvesting effort was related to the fish stock at harvest by the Gordon-Schaefer average sustainable yield model. However, when deriving the option value, the harvesting effort and fish stock at harvest were treated as unrelated to each other. I show that, when this inconsistency is rectified, the option value is smaller, and as a result the optimal harvest trigger is lower (or moves closer). Further, the optimal harvest trigger becomes less sensitive to the degree of uncertainty regarding the evolution of the fish biomass. JEL Classification Codes: Q22, C61