Reconstruction of shale image based on Wasserstein Generative Adversarial Networks with gradient penalty

 Generative Adversarial Networks (GANs), as most popular artificial intelligence models in the current image generation field, have excellent image generation capabilities. Based on Wasserstein GANs with gradient penalty, this paper proposes a novel digital core reconstruction method. First, a convolutional neural network is used as a generative network to learn the distribution of real shale samples, and then a convolutional neural network is constructed as a discriminative network to distinguish reconstructed shale samples from real ones. Through this confrontation training method, realistic digital core samples of shale can be reconstructed. The paper uses two-point covariance function, Frechet Inception Distance and Kernel Inception Distance, to evaluate the quality of digital core samples of shale reconstructed by GANs. The results show that the covariance function can test the similarity between generated and real shale samples, and that GANs can efficiently reconstruct digital core samples of shale with high-quality. Compared with multiple point statistics, the new method does not require prior inference of the probability distribution of the training data, and directly uses noise vector to generate digital core samples of shale without using constraints of "hard data" in advance. It is easy to produce an unlimited number of new samples. Furthermore, the training time is also shorter, only 4 hours in this paper. Therefore, the new method has some good points compared with current methods.Cited as: Zha, W., Li, X., Xing, Y., He, L., Li, D. Reconstruction of shale image based on Wasserstein Generative Adversarial Networks with gradient penalty. Advances in Geo-Energy Research, 2020, 4(1): 107-114, doi: 10.26804/ager.2020.01.1

Modeling Capacity-related Factors in a Theme Park Context

A comprehensive analysis among theme park capacity and all its factors is lacked in existing tourism research. Based on a case study of a Chinese theme park, this paper explores the factors that affect theme park’s capacity and the relationships among them. Two models on theme park attendance-related factors and experience-related factors were built based on empirical tests and previous studies. Attendance, waiting time and tourist satisfaction were found to be closely related to each other. Tolerable waiting time, visitors’ expectation on the number and experience value of attractions visited can be effective indicators used to determine appropriate capacity of theme park. In the end, this paper conceptualized a comprehensive model of the relationships among theme park attendance, capacity, visitor waiting time, expectation on experience, satisfaction, and other related variables, based on the research findings and literature review

An Operational Statistical Scheme for Tropical Cyclone-Induced Rainfall Forecast

Nonparametric methods are used in this study to analyze and predict short-term rainfall due to tropical cyclones (TCs) in a coastal meteorological station. All 427 TCs during 1953–2011, which made landfall along the Southeast China coast with a distance less than 700 km to a certain meteorological station, Shenzhen, are analyzed and grouped according to their landfalling direction, distance, and intensity. The corresponding daily rainfall records at Shenzhen Meteorological Station (SMS) during TCs landfalling period (a couple of days before and after TC landfall) are collected. The maximum daily rainfall (R24) and maximum 3-day accumulative rainfall (R72) records at SMS for each TC category are analyzed by a nonparametric statistical method, percentile estimation. The results are plotted by statistical boxplot, expressing in the probability of precipitation. The performance of the statistical boxplots was evaluated to forecast the short-term rainfall at SMS during the TC seasons in 2012 and 2013. The results show that the boxplot scheme can be used as a valuable reference to predict the short-term rainfall at SMS due to TCs landfalling along the Southeast China coast

Surface passivation for highly active, selective, stable, and scalable CO2 electroreduction

Electrochemical conversion of CO2 to formic acid using Bismuth catalysts is one the most promising pathways for industrialization. However, it is still difficult to achieve high formic acid production at wide voltage intervals and industrial current densities because the Bi catalysts are often poisoned by oxygenated species. Herein, we report a Bi3S2 nanowire-ascorbic acid hybrid catalyst that simultaneously improves formic acid selectivity, activity, and stability at high applied voltages. Specifically, a more than 95% faraday efficiency was achieved for the formate formation over a wide potential range above 1.0 V and at ampere-level current densities. The observed excellent catalytic performance was attributable to a unique reconstruction mechanism to form more defective sites while the ascorbic acid layer further stabilized the defective sites by trapping the poisoning hydroxyl groups. When used in an all-solid-state reactor system, the newly developed catalyst achieved efficient production of pure formic acid over 120 hours at 50 mA cm–2 (200 mA cell current)

Transcranial electrical stimulation motor threshold can estimate individualized tDCS dosage from reverse-calculation electric-field modeling

Background Unique amongst brain stimulation tools, transcranial direct current stimulation (tDCS) currently lacks an easy or widely implemented method for individualizing dosage. Objective We developed a method of reverse-calculating electric-field (E-field) models based on Magnetic Resonance Imaging (MRI) scans that can estimate individualized tDCS dose. We also evaluated an MRI-free method of individualizing tDCS dose by measuring transcranial magnetic stimulation (TMS) motor threshold (MT) and single pulse, suprathreshold transcranial electrical stimulation (TES) MT and regressing it against E-field modeling. Key assumptions of reverse-calculation E-field modeling, including the size of region of interest (ROI) analysis and the linearity of multiple E-field models were also tested. Methods In 29 healthy adults, we acquired TMS MT, TES MT, and anatomical T1-weighted MPRAGE MRI scans with a fiducial marking the motor hotspot. We then computed a “reverse-calculated tDCS dose” of tDCS applied at the scalp needed to cause a 1.00 V/m E-field at the cortex. Finally, we examined whether the predicted E-field values correlated with each participant’s measured TMS MT or TES MT. Results We were able to determine a reverse-calculated tDCS dose for each participant using a 5 × 5 x 5 voxel grid region of interest (ROI) approach (average = 6.03 mA, SD = 1.44 mA, range = 3.75–9.74 mA). The Transcranial Electrical Stimulation MT, but not the Transcranial Magnetic Stimulation MT, significantly correlated with the ROI-based reverse-calculated tDCS dose determined by E-field modeling (R2= 0.45, p \u3c 0.001). Conclusions Reverse-calculation E-field modeling, alone or regressed against TES MT, shows promise as a method to individualize tDCS dose. The large range of the reverse-calculated tDCS doses between subjects underscores the likely need to individualize tDCS dose. Future research should further examine the use of TES MT to individually dose tDCS as an MRI-free method of dosing tDCS

XV. brain stimulation therapeutics

This chapter covers how repetitive transcranial magnetic stimulation (rTMS) or transcranial direct current stimulation (tDCS) presently affects smoking cessation. 14 human studies have examined the efficacy of rTMS on cue craving, cigarette consumption, or smoking cessation using a variety of different coils, locations, and treatment parameters. These studies included 7 randomized-controlled trials (RCT) and 7 experimental studies. Most studies (12/14) reported that rTMS reduced cue-induced craving, 5 showed that it decreased cigarette consumption, and 3/4 reported that multiple sessions of rTMS increased the quit rate. In contrast to rTMS, tDCS has 6 RCT studies, of which only 2 studies reported that tDCS reduced craving, and only 1 reported that it reduced cigarette consumption. Three studies failed to find an effect of tDCS on cravings. No tDCS studies reported changing quitting rates in people who smoke. Despite the early positive results of tDCS on nicotine dependence symptoms, 2 larger RCTs recently failed to find a therapeutic effect of tDCS for smoking cessation. In conclusion, rTMS studies demonstrate that multiple sessions help quit smoking, and it has gained FDA approval for that purpose. However, more studies are needed to examine the effect of tDCS with different treatment parameters

Theoretical analysis of hydrogen solubility in direct coal liquefaction solvents

Abstract The cyclic hydrogenation technology in a direct coal liquefaction process relies on the dissolved hydrogen of the solvent or oil participating in the hydrogenation reaction. Thus, a theoretical basis for process optimization and reactor design can be established by analyzing the solubility of hydrogen in liquefaction solvents. Experimental studies of hydrogen solubility in liquefaction solvents are challenging due to harsh reaction conditions and complex solvent compositions. In this study, the composition and content of liquefied solvents were analyzed. As model compounds, hexadecane, toluene, naphthalene, tetrahydronaphthalene, and phenanthrene were chosen to represent the liquefied solvents in chain alkanes and monocyclic, bicyclic, and tricyclic aromatic hydrocarbons. The solubility of hydrogen X (mol/mol) in pure solvent components and mixed solvents (alkanes and aromatics mixed in proportion to the chain alkanes + bicyclic aromatic hydrocarbons, bicyclic saturated aromatic hydrocarbons + bicyclic aromatic hydrocarbons, and bicyclic aromatic hydrocarbons + compounds containing heteroatoms composed of mixed components) are determined using Aspen simulation at temperature and pressure conditions of 373–523 K and 2–10 MPa. The results demonstrated that at high temperatures and pressures, the solubility of hydrogen in the solvent increases with the increase in temperature and pressure, with the pressure having a greater impact. Furthermore, the results revealed that hydrogen is more soluble in straight-chain alkanes than in other solvents, and the solubility of eicosanoids reaches a maximum of 0.296. The hydrogen solubility in aromatic ring compounds decreased gradually with an increase in the aromatic ring number. The influence of chain alkanes on the solubility of hydrogen predominates in a mixture of solvents with different mixing ratios of chain alkanes and aromatic hydrocarbons. The solubility of hydrogen in mixed aromatic solvents is less than that in the corresponding single solvents. Hydrogen is less soluble in solvent compounds containing heteroatoms than in compounds without heteroatoms

Impact of Model Resolution on Secondary Eyewall Formation and Eyewall Replacement Cycle

Abstract Depicting Secondary Eyewall Formation (SEF) and Eyewall Replacement Cycle (ERC) in a numerical model is important for tropical cyclone (TC) forecasting. However, there is no consensus about what resolutions are appropriate to describe SEF/ERC within a full‐physics mesoscale model. In this study, numerical experiments are conducted to examine the impact of the horizontal and vertical resolutions on SEF/ERC. The mesoscale model is configured through nesting to the horizontal grid spacings of 6, 4, 2, 1.33, 0.67‐km, and with 27‐ and 54‐levels on an f‐plane in a quiescent environment. In addition, there are more levels below 1.5‐km to better describe the TC boundary layer (TCBL). The simulations with 6 and 4‐km grid spacings show no obvious SEF/ERC regardless of the number of vertical levels. When the horizontal grid spacings decrease to 2‐km or smaller, the simulations manifest SEF/ERC. These results are supported by a few simulations with the ARW model using similar configurations. Furthermore, the spectra of kinetic energy and vertical velocity from various resolutions confirm that the grid spacings should be smaller than 4‐km to resolve SEF/ERC. The impact of doubling vertical levels on the SEF/ERC is not as significant as doubling the horizontal resolutions. Finally, we discuss the coupling between the balanced/unbalanced flows (above/in the TCBL), and their effect on SEF. It is proposed that the coupled balanced/unbalanced processes that generate the quasi‐steady cooling zone in the primary eyewall and two warming regions inside and beyond the cooling zone are essential for SEF