Exposure of a small amount of oxygen/air (microaeration) has been reported to benefit the anaerobic digestion (AD) process in enhancing hydrolysis, improving methane yield, stabilizing the process ...and scavenging hydrogen sulfide among others. The underlying mechanism of enhancing AD process via microaeration is the augmentation of activity and diversity of the microbial consortia that promotes syntrophic interactions among different microbial groups, thereby creating a more stable process. To design and implement a microaeration-based AD process, fundamental insights about the mechanism of the AD system at process, microbial and molecular levels must be fully explored. This review critically examines microaeration-based AD processes through our recent understandings of the effect of oxygen on microbial community structure, enzymatic, energetic, physiological, and biochemical aspects of the microbial-mediated process. Syntrophic interactions between hydrolytic, fermentative, sulfate reducing, syntrophic bacteria and methanogens under microaerobic conditions are examined to reveal putative mechanism and factors that need to be considered when implementing microaeration in AD process. Further studies are needed to better understand the microbial pathways and bioenergetics of the microaerobic AD process by adopting advanced molecular techniques such as metagenomics, transcriptomics, and proteomics.
•Oxidation-reduction potential is a reliable approach to precisely control microaeration in anaerobic digestion (AD) process.•Anaerobes survive under a microaerobic condition with various anti-oxidative mechanisms.•Microaeration promotes microbial activity, diversity, and syntrophy in AD processes.•Anaerobic processes under microaerobic conditions need in depth evaluation at molecular level.
•A facile manufacturing process of new Rb2CO3-decorated In2O3 sensor.•The Rb2CO3/In2O3 composite sensor detecting 100 ppb level NO2 gas at room temperature under visible light illumination.•Propose ...the mechanism for the high sensing performance realized by high rate of electron supply to the receptor.•Good sensing performance of high selectivity, stability, repeatability, linearity, with discussion of the humidity effect.
The Rb2CO3-decorated In2O3 sensor is prepared for detection of NO2 at room temperature under light irradiation. Physical and chemical properties of the materials and structures are thoroughly investigated by various analytical tools of scanning electron microscopy, transmission electron microscopy, X-ray diffraction, X-ray photoelectron spectroscopy, and Raman spectroscopy, thereby confirming the formation of the Rb2CO3/In2O3 p-n junction at the interface. The Rb2CO3-decoration effect on In2O3 sensor is examined under light irradiation of different wavelengths and intensities. Rb2CO3-decoration exhibits much higher sensing performance than pure In2O3 sensor, and furthermore, the visible light irradiation improves in the response level and sensing kinetics. The sensor detects less than 100 ppb NO2. In addition, the Rb2CO3-decorated In2O3 sensor shows high selectivity, stability, repeatability, and linearity. The ultimate performance of the nanostructured sensor is elucidated by the depletion model of the conduction type gas sensors. The effect of humidity on the sensing performance is also investigated.
Photodynamic therapy (PDT) has been considered a noninvasive and cost-effective modality for tumor treatment. However, the complexity of tumor microenvironments poses challenges to the implementation ...of traditional PDT. Here, we review recent advances in PDT to resolve the current problems. Major breakthroughs in PDTs are enabling significant progress in molecular medicine and are interconnected with innovative strategies based on smart bio/nanomaterials or therapeutic insights. We focus on newly developed PDT strategies designed by tailoring photosensitive reactive oxygen species generation, which include the use of proteinaceous photosensitizers, self-illumination, or oxygen-independent approaches. While these updated PDT platforms are expected to enable major advances in cancer treatment, addressing future challenges related to biosafety and target specificity is discussed throughout as a necessary goal to expand the usefulness of PDT.
Although algebraic graph theory-based models have been widely applied in physical modeling and molecular studies, they are typically incompetent in the analysis and prediction of biomolecular ...properties, confirming the common belief that “one cannot hear the shape of a drum”. A new development in the century-old question about the spectrum–geometry relationship is provided. Novel algebraic graph learning score (AGL-Score) models are proposed to encode high-dimensional physical and biological information into intrinsically low-dimensional representations. The proposed AGL-Score models employ multiscale weighted colored subgraphs to describe crucial molecular and biomolecular interactions in terms of graph invariants derived from graph Laplacian, its pseudo-inverse, and adjacency matrices. Additionally, AGL-Score models are integrated with an advanced machine learning algorithm to predict biomolecular macroscopic properties from the low-dimensional graph representation of biomolecular structures. The proposed AGL-Score models are extensively validated for their scoring power, ranking power, docking power, and screening power via a number of benchmark datasets, namely CASF-2007, CASF-2013, and CASF-2016. Numerical results indicate that the proposed AGL-Score models are able to outperform other state-of-the-art scoring functions in protein–ligand binding scoring, ranking, docking, and screening. This study indicates that machine learning methods are powerful tools for molecular docking and virtual screening. It also indicates that spectral geometry or spectral graph theory has the ability to infer geometric properties.
The high strength and hard-to-cut materials can be easily machined by using modern machining methods. It is important to introduce the efforts on enhancing the process for improving the machining ...quality. In the present investigation, an effort was made to analyze the effects of micro-size Al2O3 particles mixed into Kerosene as the dielectric under different powder concentrations on machining titanium alloy in Electrical Discharge Machining (EDM). Response Surface Methodology (RSM) based algorithm utilized to analyze the performance measures by considering machining time with Cost of Goods Manufactured (COGM) method in Powder Mixed Electro Discharge Machining (PMEDM) process. It was found that the micron size powders can significantly help to enhance the surface quality of the Ti-6Al-4V during machining in the PMEDM process. The presence of carbon, oxygen elements and the formation of surface oxides and carbides has been found due to the decomposition of dielectric fluid in the PMEDM process. The lower deep cavities and uniform of PMEDM’s surface have been produced by added Al2O3 powder into the EDM process owing to lower surface cracks density, conductivity.
Deep CNN-Based Blind Image Quality Predictor Kim, Jongyoo; Nguyen, Anh-Duc; Lee, Sanghoon
IEEE transaction on neural networks and learning systems,
2019-Jan., 2019-01-00, 2019-1-00, 20190101, Volume:
30, Issue:
1
Journal Article
Image recognition based on convolutional neural networks (CNNs) has recently been shown to deliver the state-of-the-art performance in various areas of computer vision and image processing. ...Nevertheless, applying a deep CNN to no-reference image quality assessment (NR-IQA) remains a challenging task due to critical obstacles, i.e., the lack of a training database. In this paper, we propose a CNN-based NR-IQA framework that can effectively solve this problem. The proposed method-deep image quality assessor (DIQA)-separates the training of NR-IQA into two stages: 1) an objective distortion part and 2) a human visual system-related part. In the first stage, the CNN learns to predict the objective error map, and then the model learns to predict subjective score in the second stage. To complement the inaccuracy of the objective error map prediction on the homogeneous region, we also propose a reliability map. Two simple handcrafted features were additionally employed to further enhance the accuracy. In addition, we propose a way to visualize perceptual error maps to analyze what was learned by the deep CNN model. In the experiments, the DIQA yielded the state-of-the-art accuracy on the various databases.
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Arsenic contamination is the most pressing issue for human health and the environment. Understanding its behavior and distribution in natural waters and developing potential materials ...for its removal plays a crucial role in tackling this problem. This review aims to present one of the most effective As removal methods: Metal–organic framework (MOF) and MOF-based materials (mMOF) adsorption and their integration into membranes. This study compiles the latest worldwide distribution of As in natural waters. Moreover, we indicate the optimal operational conditions and feasibility of the composites for As removal. This study provides a comprehensive report on the mechanism of As adsorption using MOFs, the development of mMOFs, and their integration into membranes to remove As species from a solution effectively. We also suggest perspectives on future research and development strategies for eco-friendly MOFs and mMOFs. Additionally, statistical methods are applied for the first time to evaluate their benefits compared with conventional adsorbents by considering multiple criteria that affect adsorption performance. Overall, mMOFs demonstrate more excellent properties than MOFs, such as high reusability and stability, short equilibrium time, applicability to removing both types of As, and remarkable interference tolerance. However, both MOFs and mMOFs are generally considered promising adsorbents over conventional adsorbents that can be effectively employed for practical water treatment applications by either adsorption or membrane processes in the near future.
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•ZnCo2O4 nanoparticles designed to decorate BiVO4 nanoworms to form n-p heterojunction.•Impressive 4.4 fold increase in the photocurrent density was achieved for ...composite.•Incorporation of ZnCo2O4 accelerates the interfacial kinetics of BiVO4.•Establish correlation between PEC and band structure analysis of the photoelectrodes.•Understanding the surface kinetics of different photoelectrodes was developed.
During the past few decades, photoelectrochemical (PEC) water splitting has attracted significant attention because of the reduced production cost of hydrogen obtained by utilizing solar energy. Significant efforts have been invested by the scientific community to produce stable ternary metal oxide semiconductors, which can enhance the stability and increase the overall production of oxygen. Herein, we present the ternary metal oxide deposition of ZnCo2O4 as a route to obtain a novel photocatalyst layer on BiVO4 to form BiVO4/ZnCo2O4 a novel composite photoanode for PEC water splitting. The structural, topographical, and optical analyses were performed using field emission scanning electron microscopy, X-ray diffraction, high-resolution transmission electron microscopy, and UV–Vis spectroscopy to confirm the structure of the ZnCo2O4 grafted over BiVO4. A remarkable 4.4-fold enhancement of the photocurrent was observed for the BiVO4/ZnCo2O4 composite compared with bare BiVO4 under visible illumination. The optimum loading of ZnCo2O4 over BiVO4 yields unprecedented stable photocurrent density with an apparent cathodic shift of 0.46 V under 1.5 AM simulated light illumination. This is also evidenced by the flat-band potential change through Mott–Schottky analysis, which reveals the formation of p-ZnCo2O4 on n-BiVO4. The improvement in the PEC performance of the composite with respect to bare BiVO4 is ascribed to the formation of thin passivating layer of p-ZnCo2O4 on n-BiVO4 which improves the kinetics of interfacial charge transfer. Based on our study, we have gained an in-depth understanding of the BiVO4/ZnCo2O4 composite as high potential in efficient PEC water splitting devices.
Abstract
New solid solution of Na
0.5
Bi
0.5
TiO
3
with BaFeO
3−
δ
materials were fabricated by sol–gel method. Analysis of X-ray diffraction patterns indicated that BaFeO
3−
δ
materials existed as a ...well solid solution and resulted in distortion the structure of host Na
0.5
Bi
0.5
TiO
3
materials. The randomly incorporated Fe and Ba cations in the host Na
0.5
Bi
0.5
TiO
3
crystal decreased the optical band gap from 3.11 to 2.48 eV, and induced the room-temperature ferromagnetism. Our density-functional theory calculations further suggested that both Ba for Bi/Na-site and Fe dopant, regardless of the substitutional sites, in Na
0.5
Bi
0.5
TiO
3
lead to the induced magnetism, which is illustrated in terms of the exchange splitting between spin subbands through the crystal field theory and Jahn–Teller distortion effects. Our work proposes a simple method for fabricating lead-free ferroelectric materials with ferromagnetism property for multifunctional applications in smart electronic devices.
We employ the time-varying copula approach to investigate the conditional dependence between the Brent crude oil price and stock markets in the Central and Eastern European (CEE) transition ...economies. Our results show evidence of a positive dependence between the oil and the stock markets of the six CEE countries, which is indicative of a contagion between those markets, regardless of the changes in the oil price or the CEE stock index. Moreover, the dependence patterns in both the center and left tails of the return distributions change over time, particularly during the heart of the financial crisis, and are best described by the Survival Gumbel copulas. The empirical evidence also suggests that the lower tail dependence is much stronger than that of the upper tail, highlighting the importance of contagion during severe contractionary business cycles. Among the sample markets, Poland is shown to be particularly sensitive in this regard, while Hungary and Slovenia are the least sensitive.
•We investigate the oil–equity relationships for six CEE transition economies.•A time-varying copula approach is proposed to describe the dependence structure.•We find evidence of a positive dependence between the oil and the stock markets.•The dependence patterns change over time, particularly during the recent crisis.•The lower tail dependence is much stronger than that of the upper tail.
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