Occurrence Regularity of Methane Gas Molecules in Composite Nanopores: A Molecular Simulation Study

AbstractTo understand the occurrence regularity of methane gas molecules in composite nanopores, the effects of temperature, pressure, size of nanopore, and burial depth on the occurrence state of methane were studied theoretically by using the grand canonical Monte Carlo and molecular dynamic simulation methods. By comparing the results available in the literature, the reasons for the difference in the occurrence states of methane molecules in nanopores were analyzed, and a reasonable occurrence regularity of methane was proposed, which provides corresponding suggestions for the actual exploitation of shale gas. The results indicated that the methane gas molecules existed in nanopore only in the adsorption and transition states under different environmental conditions. They were preferentially adsorbed at the strong adsorption sites on the nanopore surface to form a stable adsorption layer. After the adsorption layer reached saturation, a transition layer with higher density than that of bulk methane was formed at the nanopore center. The total adsorption capacity of methane decreased gradually with an increase in the internal temperature of shale reservoirs and increased with an increase in nanopore size. In addition, the average amount of methane stored in the nanopore increased at a deeper burial depth. The occurrence state of methane under different pressure ranges was controlled under different action mechanisms. Under low pressure (P<20 MPa), the adsorption of methane molecules was controlled by the number of strong adsorption sites on the nanopore surface, where the density peak intensity of the adsorption layer increased with the pressure. However, under high pressure (P>20 MPa), the adsorption was controlled by the diffusion process of methane molecules in the organic matter layer, where both the adsorption and transition layers reached the saturation state, and excessive methane molecules diffused deeper into the kerogen layer. The approach to effectively improve the recovery efficiency was to inject water or carbon dioxide into the shale reservoir where the water or carbon dioxide molecules occupy strong adsorption positions than the methane molecules adsorbed originally under the competitive adsorption effect, and the adsorbed methane molecules were transformed to a free state

Does Gender Make a Difference in Deception? The Effect of Transcranial Direct Current Stimulation Over Dorsolateral Prefrontal Cortex

Neuroimaging studies have indicated a correlation between dorsolateral prefrontal cortex (DLPFC) activity and deceptive behavior. We applied a transcranial direct current stimulation (tDCS) device to modulate the activity of subjects’ DLPFCs. Causal evidence of the neural mechanism of deception was obtained. We used a between-subject design in a signaling framework of deception, in which only the sender knew the associated payoffs of two options. The sender could freely choose to convey the truth or not, knowing that the receiver would never know the actual payment information. We found that males were more honest than females in the sham stimulation treatment, while such gender difference disappeared in the right anodal/left cathodal stimulation treatment, because modulating the activity of the DLPFC using right anodal/left cathodal tDCS only significantly decreased female subjects’ deception

Modulating the Activity of the DLPFC and OFC Has Distinct Effects on Risk and Ambiguity Decision-Making: A tDCS Study

Human beings are constantly exposed to two types of uncertainty situations, risk and ambiguity. Neuroscientific studies suggest that the dorsolateral prefrontal cortex (DLPFC) and the orbital frontal cortex (OFC) play significant roles in human decision making under uncertainty. We applied the transcranial direct current stimulation (tDCS) device to modulate the activity of participants’ DLPFC and OFC separately, comparing the causal relationships between people’s behaviors and the activity of the corresponding brain cortex when confronted with situations of risk and ambiguity. Our experiment employed a pre–post design and a risk/ambiguity decision-making task, from which we could calculate the preferences via an estimation model. We found evidences that modulating the activity of the DLPFC using right anodal/left cathodal tDCS significantly enhanced the participants’ preferences for risk, whereas modulating the activity of the OFC with right anodal/left cathodal tDCS significantly decreased the participants’ preferences for ambiguity. The reverse effects were also observed in the reversed tDCS treatments on the two areas. Our results suggest that decision-making processes under risk and ambiguity are complicated and may be encoded in two distinct circuits in our brains as the DLPFC primarily impacts decisions under risk whereas the OFC affects ambiguity

Reproducing Whisper-Style Training Using an Open-Source Toolkit and Publicly Available Data

Pre-training speech models on large volumes of data has achieved remarkable success. OpenAI Whisper is a multilingual multitask model trained on 680k hours of supervised speech data. It generalizes well to various speech recognition and translation benchmarks even in a zero-shot setup. However, the full pipeline for developing such models (from data collection to training) is not publicly accessible, which makes it difficult for researchers to further improve its performance and address training-related issues such as efficiency, robustness, fairness, and bias. This work presents an Open Whisper-style Speech Model (OWSM), which reproduces Whisper-style training using an open-source toolkit and publicly available data. OWSM even supports more translation directions and can be more efficient to train. We will publicly release all scripts used for data preparation, training, inference, and scoring as well as pre-trained models and training logs to promote open science.Comment: Accepted at ASRU 202

A Framework for Integrating Influence Diagrams and POMDPs

An influence diagram is a widely-used graphical model for representing and solving problems of sequential decision making under imperfect information. A closely-related model for the same class of problems is a partially observable Markov decision process (POMDP). This dissertation leverages the relationship between these two models to develop improved algorithms for solving influence diagrams. The primary contribution is to generalize two classic dynamic programming algorithms for solving influence diagrams, Arc Reversal and Variable Elimination, by integrating them with a dynamic programming technique originally developed for solving POMDPs. This generalization relaxes constraints on the ordering of the steps of these algorithms in a way that dramatically improves scalability, especially in solving complex, multi-stage decision problems. A secondary contribution is the adoption of a more compact and intuitive representation of the solution of an influence diagram, called a strategy. Instead of representing a strategy as a table or as a tree, a strategy is represented as an acyclic graph, which can be exponentially more compact, making the strategy easier to interpret and understand

Analysis of the Historical System of the Wenzhou Model : A View from the Perspective of Personalized Transaction and Nonpersonalized Transaction

This article uses the theoretical analysis of the historical system used to study the trade and industry of the Wenzhou model, which expounds three important issues about Wenzhou's economy and its people's economy from the perspective of personalized transaction and nonpersonalized transaction, and points out a possible trend in the development of Wenzhou's methodology.

The performance of industrial productivity across regions of China: Structural differences, institutional shocks and dynamic characteristics

This paper is dedicated to probing into the dynamic performances of industrial productivity across regions of transitional China, using the panel data of provincial level. Based on the approach by Kumbhakar (2000), TFP (total factor productivity) growth is decomposed into four components. The main results are as follows. First, since 1988, the industrial TFP growth has been commonly accelerated across regions, with a rising technical change rate as the principal impetus. Second, meanwhile, technical efficiency and factors’ allocative efficiency are deteriorated with scale efficiency switching from being retrogressive to being progressive. Third, although the SOE (state-owned enterprise) reform in the late 1990s has constituted a common shock to the industrial productivity, the eastern area with relatively few SOEs suffers the least from this policy enforcement. Fourth, by exploring the sources of productivity differences, we further confirm that the institutional shock launched by SOE reform in the late 1990s is crucial for the enhancement of scale effects as well as the temporarily rapid decline of factors’ allocative efficiency; in addition, the educational level of the labor-force and the share of non-SOEs in the industrial output contribute positively to the acceleration of technical change and the improvement of allocative efficiency. The economic transition, accompanied by gradual institutional reforms, is reshaping the map of regional industrialization through various channels

Industrial productivity performance in Chinese regions (1987–2002): a decomposition approach

This article investigates the productivity performance of China's industries 1987–2002, by means of a provincial panel. Productivity growth is decomposed into four components: technical progress, scale efficiezncy change, and improvements in technical and allocative efficiency. Although total factor productivity growth had been the second major contributor to industrial growth (after capital accumulation), it has been driven mainly by technical progress rather than efficiency improvement. The estimated stochastic production frontier function exhibits substantial economies of scale. Regional differences in technical progress are negligible, but differences in technical efficiency are statistically significant across regions. The restructuring of state-owned enterprises from the mid-1990s seems to have improved technical efficiency considerably, while the performance of allocative efficiency does not seem to be converging towards standard conditions for optimizing firms under perfect competition. Factor price distortions, like artificially cheap capital together with suppressed wage levels, might have been the driving forces behind China's capital-intensive industrial growth and technology-dependent productivity performance