Petrographic Characteristics and Depositional Environment Evolution of Middle Miocene Sediments in the Thien Ung - Mang Cau Structure of Nam Con Son Basin

This paper introduces the petrographic characteristics and depositional environment of Middle Miocene rocks of the Thien Ung - Mang Cau structure in the central area of Nam Con Son Basin based on the results of analyzing thin sections and structural characteristics of core samples. Middle Miocene sedimentary rocks in the studied area can be divided into three groups: (1) Group of terrigenous rocks comprising greywacke sandstone, arkosic sandstone, lithic-quartz sandstone, greywacke-lithic sandstone, oligomictic siltstone, and bitumenous claystone; (2) Group of carbonate rocks comprising dolomitic limestone and bituminous limestone; (3) Mixed group comprising calcareous sandstone, calcarinate sandstone, arenaceous limestone, calcareous claystone, calcareous silty claystone, dolomitic limestone containing silt, and bitumen. The depositional environment is expressed through petrographic characteristics and structure of the sedimentary rocks in core samples. The greywacke and arkosic sandstones are of medium grain size, poor sorting and roundness, and siliceous cement characterizing the alluvial and estuarine fan environment expressed by massive structure of core samples. The mixed calcareous limestone, arenaceous dolomitic limestone, and calcareous and bituminous clayey siltstone in the core samples are of turbulent flow structure characterizing shallow bay environment with the action of bottom currents. The dolomitic limestones are of relatively homogeneous, of microgranular and fine-granular texture, precipitated in a weakly reducing, semi-closed, and relatively calm bay environment

Titanium dioxide - activated carbon composite for photoelectrochemical degradation of phenol

In this study, titanium dioxide (TiO2) and titanium dioxide – activated carbon composite (TiO2–AC) were prepared by sol-gel method for photoelectrochemical (PEC) applications. Characterization of the materials was performed by scanning electron microscope, energy dispersive X-ray analysis, Fourier transform infrared spectroscopy, X-ray diffraction, and diffuse reflectance spectroscopy. The results show that TiO2 was successfully loaded on activated carbon (AC), producing TiO2–AC with 2.61 eV of bandgap energy, lower than that of TiO2 (3.15 eV). Photoanodes based on TiO2 and TiO2–AC were fabricated and applied to PEC experiments for phenol degradation. In comparison with the TiO2 photoanode, the TiO2–AC one exhibited superior photocatalytic activity, which was indicated by a high current density and effective phenol removal. A mechanism of phenol PEC degradation on the TiO2–AC photoanode was proposed, which includes interaction between protonated phenol and active sites bearing oxygen on the photoanode surface. A kinetic model according to this mechanism was also established and fitted to experimental findings, resulting in rate constants of elementary reactions

Class based Influence Functions for Error Detection

Influence functions (IFs) are a powerful tool for detecting anomalous examples in large scale datasets. However, they are unstable when applied to deep networks. In this paper, we provide an explanation for the instability of IFs and develop a solution to this problem. We show that IFs are unreliable when the two data points belong to two different classes. Our solution leverages class information to improve the stability of IFs. Extensive experiments show that our modification significantly improves the performance and stability of IFs while incurring no additional computational cost.Comment: Thang Nguyen-Duc, Hoang Thanh-Tung, and Quan Hung Tran are co-first authors of this paper. 12 pages, 12 figures. Accepted to ACL 202

The novel method to reduce the silica content in lignin recovered from black liquor originating from rice straw

Difficulties in the production of lignin from rice straw because of high silica content in the recovered lignin reduce its recovery yield and applications as bio-fuel and aromatic chemicals. Therefore, the objective of this study is to develop a novel method to reduce the silica content in lignin from rice straw more effectively and selectively. The method is established by monitoring the precipitation behavior as well as the chemical structure of precipitate by single-stage acidification at different pH values of black liquor collected from the alkaline treatment of rice straw. The result illustrates the significant influence of pH on the physical and chemical properties of the precipitate and the supernatant. The simple two-step acidification of the black liquor at pilot-scale by sulfuric acid 20w/v% is applied to recover lignin at pH 9 and pH 3 and gives a percentage of silica removal as high as 94.38%. Following the developed process, the high-quality lignin could be produced from abundant rice straw at the industrial-scale

Research Trends in Evidence-Based Medicine: A Joinpoint Regression Analysis of More than 50 Years of Publication Data

Background Evidence-based medicine (EBM) has developed as the dominant paradigm of assessment of evidence that is used in clinical practice. Since its development, EBM has been applied to integrate the best available research into diagnosis and treatment with the purpose of improving patient care. In the EBM era, a hierarchy of evidence has been proposed, including various types of research methods, such as meta-analysis (MA), systematic review (SRV), randomized controlled trial (RCT), case report (CR), practice guideline (PGL), and so on. Although there are numerous studies examining the impact and importance of specific cases of EBM in clinical practice, there is a lack of research quantitatively measuring publication trends in the growth and development of EBM. Therefore, a bibliometric analysis was constructed to determine the scientific productivity of EBM research over decades. Methods NCBI PubMed database was used to search, retrieve and classify publications according to research method and year of publication. Joinpoint regression analysis was undertaken to analyze trends in research productivity and the prevalence of individual research methods. Findings Analysis indicates that MA and SRV, which are classified as the highest ranking of evidence in the EBM, accounted for a relatively small but auspicious number of publications. For most research methods, the annual percent change (APC) indicates a consistent increase in publication frequency. MA, SRV and RCT show the highest rate of publication growth in the past twenty years. Only controlled clinical trials (CCT) shows a non-significant reduction in publications over the past ten years. Conclusions Higher quality research methods, such as MA, SRV and RCT, are showing continuous publication growth, which suggests an acknowledgement of the value of these methods. This study provides the first quantitative assessment of research method publication trends in EBM

The novel method to reduce the silica content in lignin recovered from black liquor originating from rice straw

Difficulties in the production of lignin from rice straw because of high silica content in the recovered lignin reduce its recovery yield and applications as bio-fuel and aromatic chemicals. Therefore, the objective of this study is to develop a novel method to reduce the silica content in lignin from rice straw more effectively and selectively. The method is established by monitoring the precipitation behavior as well as the chemical structure of precipitate by single-stage acidification at different pH values of black liquor collected from the alkaline treatment of rice straw. The result illustrates the significant influence of pH on the physical and chemical properties of the precipitate and the supernatant. The simple two-step acidification of the black liquor at pilot-scale by sulfuric acid 20w/v% is applied to recover lignin at pH 9 and pH 3 and gives a percentage of silica removal as high as 94.38%. Following the developed process, the high-quality lignin could be produced from abundant rice straw at the industrial-scale

Leveraging deep neural networks for massive MIMO data detection

Abstract Massive multiple-input multiple-output (MIMO) is a key technology for emerging next-generation wireless systems. Utilizing large antenna arrays at base-stations, massive MIMO enables substantial spatial multiplexing gains by simultaneously serving a large number of users. However, the complexity in massive MIMO signal processing (e.g., data detection) increases rapidly with the number of users, making conventional hand-engineered algorithms less computationally efficient. Lowcomplexity massive MIMO detection algorithms, especially those inspired or aided by deep learning, have emerged as a promising solution. While there exist many MIMO detection algorithms, the aim of this magazine paper is to provide insight into how to leverage deep neural networks (DNN) for massive MIMO detection. We review recent developments in DNN-based MIMO detection that incorporate the domain knowledge of established MIMO detection algorithms with the learning capability of DNNs. We then present a comparison of the key numerical performance metrics of these works. We conclude by describing future research areas and applications of DNNs in massive MIMO receivers