Voluntary Environmental Regulations and Firm Innovation in China

The world is fighting the issue of increasing levels of pollution and the detrimental effects of the ecological imprints of the business industry. Pollution and other environmental issues are causing the environment to deteriorate; however, this has also led to an increased interest in the protection of the environment. The Porter hypothesis has stimulated a long debate on whether organizational regulations can lead to changes in a firm’s innovation. Building on these theories, this study evaluates the effect of voluntary environmental regulations (VERs) on the innovation of Chinese firms. For this purpose, the study uses a dichotomous dependent variable. The proxy variables used for evaluating the innovative performance of firms are the average investments made for research and development (R&D) activities, which are evaluated on the basis of investments made and the decision to invest in innovation activities. VERs were evaluated using applications for the ISO 14000 certification. The study uses firm level variables to answer the research questions, as well as control variables such as firm size, profitability, degree of competition, and high technology industry. The results of the estimations reveal that the impact of environmental regulations (ERs) is positive and significant in terms of the innovation output of the firms under consideration. Moreover, the results also highlight the fact that large firms with high levels of profitability and a presence in the technology sector are more adept at introducing innovative activities. The study also provides some policy implications

Enhanced Hydrocarbon Recovery In Tight And Shale Reservoirs Using Surfactants And Supercritical CO2

The tight and shale oil reservoirs have been becoming increasingly important energy resources. However, the flow and storage mechanisms of oil in tight and shale reservoirs are still ambiguous. The inorganic pores, organic pores, and kerogen skeleton in shale oil reservoirs can store hydrocarbons. Besides, the oil recovery of tight and shale oil reservoirs is extremely low even with the assistance of hydraulic fracturing and horizontal drilling. The surfactants and CO2 are commonly used to enhance the oil recovery in tight and shale reservoirs. A comprehensive review of CO2 and surfactants EOR is shown in Chapter Ⅱ. The spontaneous imbibition is a very important mechanism for oil production from fractured reservoirs. The counter-current spontaneous imbibition experiments and nuclear magnetic resonance (NMR) were combined to study the imbibition and the fluid distribution in eight core samples. NMR is able to detect fluid distribution in different sizes of pores ranging from micropores to fractures. Before the experiments, the Middle Bakken and Berea cores were saturated with air. Then imbibition experiments with one end open (OEO) and two ends closed (TEC) boundary conditions were carried out. The numerical solutions of spontaneous imbibition models match quite well with experimental results by adjusting model parameters. The capillary pressure and relative permeability curves were obtained from the matching. The imbibition experiments, mathematical models, and nuclear magnetic resonance (NMR) results are discussed in Chapter Ⅲ. Surfactants are very common chemicals for EOR in fractured tight reservoirs. We experimentally investigated EOR using various kinds of surfactants. Six core samples were obtained from the Middle Bakken Formation in North Dakota. Before the imbibition experiment, petrophysical analyses were conducted for the samples. XRD method was used to analyze the mineral composition. Nitrogen adsorption and SEM methods were combined to study the pore size distribution and microstructures. Then I performed brine imbibition and surfactant imbibition in six Bakken cores and two Berea sandstones. Before the experiment, the cores were fully saturated with Bakken crude oil. The core plugs were then submerged into the brine and surfactant solutions with an all-face-open (AFO) condition. Experiments of brine and surfactant imbibing into oil-filled cores were carried out with the recording of recovered oil volume using imbibition cells. Different types of surfactants such as cationic, anionic, and nonionic, were tested in the study. Those experiments and the results are presented in Chapter Ⅲ. The shale oil reservoirs have much complex storage and flow mechanisms. The inorganic pores, organic pores, and kerogen matrix are important media to store water and oil in shale rocks. We present a vacuum imbibition method to identify the volume of water and oil in these media. Before the experiments, comprehensive rock characterizations were carried out on shale samples from Shahejie Formation combining various methods including N2 adsorption, scanning electron microscope (SEM), X-ray diffraction, and RockEval pyrolysis. Then, vacuum imbibition experiments were conducted on shale samples using water and n-dodecane. The accurate volumes of water in organic pores, oil in inorganic pores and organic pores, and the volume of dissolved oil were determined from vacuum imbibition experiments. The effects of thermal maturity (Ro) on shale storage were analyzed. Furthermore, novel mathematical models of oil and water vacuum imbibition in shale were proposed. The water imbibition in inorganic pores is a capillary flow. The oil imbibition in shale includes capillary flow in pore structures and diffusion in kerogen. The pore-kerogen double diffuse layer (PKDDL) physical model was proposed for the mechanisms of the hydrocarbon mass transfer between pore structures and kerogen. The capillary pressure and the dissolution rate constants were obtained by matching mathematical models with experimental results. This method is crucial for evaluating the water and oil storage and transfer in organic-rich shale and advances the crucial mechanisms for the evaluation and development of shale reservoirs. The experimental method, mathematical models, and results of the vacuum imbibition study are in Chapter Ⅳ. The oil recovery of shale reservoirs is very low due to the extremely low permeability and the existence of organic matter. CO2 injection in shale oil reservoirs is a feasible method for CO2 geological sequestration and enhanced oil recovery. However, the mechanisms of mass transfer in inorganic pores and organic matter (kerogen) are still ambiguous. Thus, the mechanisms of diffusion and extraction were investigated. A novel pore-kerogen diffuse layer (PKDL) model was proposed for mass transfer between kerogen matrix and pores (inorganic pores and organic pores). Mathematical models for hydrocarbon mass transfer in spherical and cylindrical shaped rocks were derived. The predicted responses of the mathematical models closely matched the experimental data of CO2 injection experiments. Hydrocarbon recovery of shales shows a delayed effect compared to tight rocks due to the additional extraction process between supercritical CO2 and kerogen. Hydrocarbons were extracted out of the kerogen matrix and then diffused through the inorganic and organic pores. This theoretical research advances the diffusion and extraction theories in shale oil. The models and results are shown in Chapter Ⅴ

Why Are Saving Rates So High in China?

In this paper, we define "The Chinese Saving Puzzle" as the persistently high national saving rate at 34-53 percent of gross domestic product (GDP) in the past three decades and a surge in the saving rate by 11 percentage points from 2000-2008. Using data from the Flow of Funds Accounts (FFA) and Urban Household Surveys (UHS) supplemented by the findings from existing studies, we analyze the sources and causes of China's high and rising saving rates in the government, corporate, and household sectors. Although the causes of China's high saving are complex, we suggest that the evolving economic, demographic, and policy trends in the internal and external environments of the Chinese economy will likely lead to a decline in national saving in the foreseeable future.demographic structure, aggregate saving, international comparison, household behavior, China

Clustering RNA structural motifs in ribosomal RNAs using secondary structural alignment

RNA structural motifs are the building blocks of the complex RNA architecture. Identification of non-coding RNA structural motifs is a critical step towards understanding of their structures and functionalities. In this article, we present a clustering approach for de novo RNA structural motif identification. We applied our approach on a data set containing 5S, 16S and 23S rRNAs and rediscovered many known motifs including GNRA tetraloop, kink-turn, C-loop, sarcin–ricin, reverse kink-turn, hook-turn, E-loop and tandem-sheared motifs, with higher accuracy than the state-of-the-art clustering method. We also identified a number of potential novel instances of GNRA tetraloop, kink-turn, sarcin–ricin and tandem-sheared motifs. More importantly, several novel structural motif families have been revealed by our clustering analysis. We identified a highly asymmetric bulge loop motif that resembles the rope sling. We also found an internal loop motif that can significantly increase the twist of the helix. Finally, we discovered a subfamily of hexaloop motif, which has significantly different geometry comparing to the currently known hexaloop motif. Our discoveries presented in this article have largely increased current knowledge of RNA structural motifs

Efficient alignment of RNA secondary structures using sparse dynamic programming

BACKGROUND: Current advances of the next-generation sequencing technology have revealed a large number of un-annotated RNA transcripts. Comparative study of the RNA structurome is an important approach to assess their biological functionalities. Due to the large sizes and abundance of the RNA transcripts, an efficient and accurate RNA structure-structure alignment algorithm is in urgent need to facilitate the comparative study. Despite the importance of the RNA secondary structure alignment problem, there are no computational tools available that provide high computational efficiency and accuracy. In this case, designing and implementing such an efficient and accurate RNA secondary structure alignment algorithm is highly desirable. RESULTS: In this work, through incorporating the sparse dynamic programming technique, we implemented an algorithm that has an O(n(3)) expected time complexity, where n is the average number of base pairs in the RNA structures. This complexity, which can be shown assuming the polymer-zeta property, is confirmed by our experiments. The resulting new RNA secondary structure alignment tool is called ERA. Benchmark results indicate that ERA can significantly speedup RNA structure-structure alignments compared to other state-of-the-art RNA alignment tools, while maintaining high alignment accuracy. CONCLUSIONS: Using the sparse dynamic programming technique, we are able to develop a new RNA secondary structure alignment tool that is both efficient and accurate. We anticipate that the new alignment algorithm ERA will significantly promote comparative RNA structure studies. The program, ERA, is freely available at http://genome.ucf.edu/ERA

Predicting folding pathways between RNA conformational structures guided by RNA stacks

Background: Accurately predicting low energy barrier folding pathways between conformational secondary structures of an RNA molecule can provide valuable information for understanding its catalytic and regulatory functions. Most existing heuristic algorithms guide the construction of folding pathways by free energies of intermediate structures in the next move during the folding. However due to the size and ruggedness of RNA energy landscape, energy-guided search can become trapped in local optima. Results: In this paper, we propose an algorithm that guides the construction of folding pathways through the formation and destruction of RNA stacks. Guiding the construction of folding pathways by coarse grained movements of RNA stacks can help reduce the search space and make it easier to jump out of local optima. RNAEAPath is able to find lower energy barrier folding pathways between secondary structures of conformational switches and outperforms the existing heuristic algorithms in most test cases. Conclusions: RNAEAPath provides an alternate approach for predicting low-barrier folding pathways between RNA conformational secondary structures. The source code of RNAEAPath and the test data sets are available at http://genome.ucf.edu/RNAEAPath

Accurate and Efficient Mapping of the Cross-Linked microRNA-mRNA Duplex Reads

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.MicroRNA (miRNA) trans-regulates the stability of many mRNAs and controls their expression levels. Reconstruction of the miRNA-mRNA interactome is key to the understanding of the miRNA regulatory network and related biological processes. However, existing miRNA target prediction methods are limited to canonical miRNA-mRNA interactions and have high false prediction rates. Other experimental methods are low throughput and cannot be used to probe genome-wide interactions. To address this challenge, the Cross-linking Ligation and Sequencing of Hybrids (CLASH) technology was developed for high-throughput probing of transcriptome-wide microRNA-mRNA interactions in vivo. The mapping of duplex reads, chimeras of two ultra-short RNA strands, poses computational challenges to current mapping and alignment methods. To address this issue, we developed CLAN (CrossLinked reads ANalysis toolkit). CLAN generated a comparable mapping of singular reads to other tools, and significantly outperformed in mapping simulated and real CLASH duplex reads, offering a potential application to other next-generation sequencing-based duplex-read-generating technologies

Learning Unmanned Aerial Vehicle Control for Autonomous Target Following

While deep reinforcement learning (RL) methods have achieved unprecedented successes in a range of challenging problems, their applicability has been mainly limited to simulation or game domains due to the high sample complexity of the trial-and-error learning process. However, real-world robotic applications often need a data-efficient learning process with safety-critical constraints. In this paper, we consider the challenging problem of learning unmanned aerial vehicle (UAV) control for tracking a moving target. To acquire a strategy that combines perception and control, we represent the policy by a convolutional neural network. We develop a hierarchical approach that combines a model-free policy gradient method with a conventional feedback proportional-integral-derivative (PID) controller to enable stable learning without catastrophic failure. The neural network is trained by a combination of supervised learning from raw images and reinforcement learning from games of self-play. We show that the proposed approach can learn a target following policy in a simulator efficiently and the learned behavior can be successfully transferred to the DJI quadrotor platform for real-world UAV control