The Rare Earth Element (REE) systematics of the post-Marinoan cap dolostones reflect the marine redox conditions and chemistry in the immediate aftermath of the snowball Earth. Rare earth elements ...and yttrium (REY) compositions in the Doushantuo cap dolostones that directly overlie Nantuo glacial diamictites in south China are determined from the inner shelf to the slope. In general, shale-normalized REY patterns (REYSN) of the cap dolostones show significant fractionations that are characterized by light REE depletion, slight middle REE enrichment relative to the light and heavy REEs, positive Eu anomalies, and slightly super-chondritic Y/Ho ratios. These dolostones, however, show no significant shale-normalized negative Ce anomalies. Such REYSN patterns are consistent with an extensive anoxic Ediacaran ocean in the shallow-to-deep water columns. The REE concentrations and normalized patterns in the cap dolostones vary spatially and stratigraphically. When compared to the shelf edge and the slope, cap dolostones from the inner shelf are distinguished by uniformly less fractionated REYSN patterns with an absence of Eu anomalies, and suppressed light REE depletion and MREE enrichment. These marked differences in the REYSN patterns indicate significant continental influences on the seawater chemistry within the restricted inner shelf (lagoon) during glacial meltdown. The temporal trends in the REY dataset of the Doushantuo cap dolostone reflect shifts in marine redox conditions. Dolostones from two thick shelf margin sections (5 m and 7 m thick, respectively) show almost identical stratigraphic trends in the REY concentrations, and the Ce, Y, and middle REE anomalies. The overall stratigraphic decrease in the Ce anomaly, accompanied by the increase in the Y anomaly and decrease in the middle REE anomaly towards the top of the rock unit, suggests that dissolved oxygen was increasing in the shallow water column during the deposition of the cap dolostones. The REE data in this study reveal that the oxygenation of the Ediacaran oceans started in the immediate aftermath of the snowball Earth (635–632 Ma).
The task of text-to-image synthesis is to generate photographic images conditioned on given textual descriptions. This challenging task has recently attracted considerable attention from the ...multimedia community due to its potential applications. Most of the up-to-date approaches are built based on generative adversarial network (GAN) models, and they synthesize images conditioned on the global linguistic representation. However, the sparsity of the global representation results in training difficulties on GANs and a shortage of fine-grained information in the generated images. To address this problem, we propose cross-modal global and local linguistic representations-based generative adversarial networks (CGL-GAN) by incorporating the local linguistic representation into the GAN. In our CGL-GAN, we construct a generator to synthesize the target images and a discriminator to judge whether the generated images conform with the text description. In the discriminator, we construct the cross-modal correlation by projecting the image representations at high and low levels onto the global and local linguistic representations, respectively. We design the hinge loss function to train our CGL-GAN model. We evaluate the proposed CGL-GAN on two publicly available datasets, the CUB and the MS-COCO. The extensive experiments demonstrate that incorporating fine-grained local linguistic information with cross-modal correlation can greatly improve the performance of text-to-image synthesis, even when generating high-resolution images.
Surface plasmon resonance (SPR) and localized SPR (LSPR) effects have been shown as the principles of some highlysensitive sensors in recent decades. Due to the advances in nano-fabrication ...technology, the plasmon nano-array sensors based on SPR and LSPR phenomena have been widely used in chemical and bioloical analysis. Sensing with surface-enhanced field and sensing for refractive index changes are able to identify the analytes quantitatively and qualitatively. With the newly developed ultrasensitive plasmonic biosensors, platforms with excellent performance have been built for various biomedical applications, including point-of-care diagnosis and personalized medicine. In addition, flexible integration of plasmonics nano-arrays and combining them with electrochemical sensing have significantly enlarged the application scenarios of the plasmonic nano-array sensors, as well as improved the sensing accuracy.
Efficient and effective data acquisition is of theoretical and practical importance in WSN applications because data measured and collected by WSN is often unreliable, such as those often accompanied ...by noise and error, missing values or inconsistent data. Motivated by fog computing, which focuses on how to effectively offload computation-intensive tasks from resource-constrained devices, this paper proposes a simple but yet effective data acquisition approach with the ability of filtering abnormal data and meeting the real-time requirement. Our method uses a cooperation mechanism by leveraging on both an architectural and algorithmic approach. Firstly, the sensor node with the limited computing resource only accomplishes detecting and marking the suspicious data using a light weight algorithm. Secondly, the cluster head evaluates suspicious data by referring to the data from the other sensor nodes in the same cluster and discard the abnormal data directly. Thirdly, the sink node fills up the discarded data with an approximate value using nearest neighbor data supplement method. Through the architecture, each node only consumes a few computational resources and distributes the heavily computing load to several nodes. Simulation results show that our data acquisition method is effective considering the real-time outlier filtering and the computing overhead.
Effective catalysts are indispensable for the preparation of fuel components from vegetable oils via the one‐step hydrothermal method. In this study, the Pt/MCM‐41 catalysts were prepared by etching ...with sulfuric acid, citric acid, or hydrochloric acid. The performance of the obtained Pt/MCM‐41 catalysts for the hydrothermal treatment of vegetable oils was evaluated in a fixed‐bed reactor using Jatropha oil as a model raw material (temperature = 360°C, liquid hourly space velocity = 1 h−1, hydrogen–oil ratio = 1000, and hydrogen pressure = 4 MPa). The modified catalysts were characterized using X‐ray diffraction, transmission electron microscopy, Brunauer–Emmett–Teller analysis, X‐ray fluorescence spectrometry, temperature‐programmed desorption of NH3, CO chemisorption, and pyridine adsorption infrared spectroscopy, respectively. Furthermore, the selectivity of various fuel components, including C8–C16 alkanes, C8–C16 iso‐alkanes, C8–C16 arenes, and C17–C18 alkanes, were analyzed based on the catalyst characteristics. Acid etching was found to decrease the surface area of Al‐MCM‐41 but increase the amount of acid sites, and among these acids, citric acid was proved as the priority additives, with better catalytic performance. Moreover, this catalyst exhibited the best conversion and the highest C8–C16 and C17–C18 selectivities.
In this study, the effect of etching with three different acids (sulfuric acid, citric acid, or hydrochloric acid) on the performance of a Pt‐loaded Al‐MCM‐41 catalyst for the one‐step hydrotreatment of vegetable oils was examined using Jatropha oil as a model raw material.
Offline Urdu Nastaleeq text recognition has long been a serious problem due to its very cursive nature. In order to get rid of the character segmentation problems, many researchers are shifting focus ...towards segmentation free ligature based recognition approaches. Majority of the prevalent ligature based recognition systems heavily rely on hand-engineered feature extraction techniques. However, such techniques are more error prone and may often lead to a loss of useful information that might hardly be captured later by any manual features. Most of the prevalent Urdu Nastaleeq test recognition was trained and tested on small sets. This paper proposes the use of stacked denoising autoencoder for automatic feature extraction directly from raw pixel values of ligature images. Such deep learning networks have not been applied for the recognition of Urdu text thus far. Different stacked denoising autoencoders have been trained on 178573 ligatures with 3732 classes from un-degraded（noise free） UPTI（Urdu Printed Text Image） data set. Subsequently, trained networks are validated and tested on degraded versions of UPTI data set. The experimental results demonstrate accuracies in range of 93% to 96% which are better than the existing Urdu OCR systems for such large dataset of ligatures.
The recognition accuracy of ligature-based Urdu language optical character recognition (OCR) systems highly depends on the accuracy of segmentation that converts Urdu text into lines and ligatures. ...In general, lines and ligatures-based Urdu language OCRs are more successful as compared to characters-based. This paper presents the techniques for segmenting Urdu Nastaleeq text images into lines and subsequently to ligatures. Classical horizontal projection-based segmentation method is augmented with a curved-line-split algorithm for successfully overcoming the problems, such as text line split position, overlapping, merged ligatures, and ligatures crossing line split positions. Ligature segmentation algorithm extracts connected components from text lines, categorizes them into primary and secondary classes, and allocates secondary components to the primary class by examining width, height, coordinates, overlapping, centroids, and baseline information. The proposed line segmentation algorithm is tested on 47 pages with 99.17% accuracy. The proposed ligature segmentation algorithm is mainly tested on a large Urdu-printed text images data set. The proposed algorithm segmented Urdu-printed text images data set to 189 000 ligatures from 10 063 text lines having 332 000 connected components. A total of about 142 000 secondary components have been successfully allocated to more than 189 000 primary ligatures with accuracy rate of 99.80%. Thus, both of the proposed segmentation algorithms outperform the existing algorithms employed for Urdu Nastaleeq text segmentation. Moreover, the proposed line segmentation algorithm is also tested on Arabic, for which it also extracted lines correctly.
Clothing image recognition has recently received considerable attention from many communities, such as multimedia information processing and computer vision, due to its commercial and social ...applications. However, the large variations in clothing images' appearances and styles and their complicated formation conditions make the problem challenging. In addition, a generic treatment with convolutional neural networks (CNNs) cannot provide a satisfactory solution considering the training time and recognition performance. Therefore, how to balance those two factors for clothing image recognition is an interesting problem. Motivated by the fast training and straightforward solutions exhibited by extreme learning machines (ELMs), in this paper, we propose a recognition framework that is based on multiple sources of features and ELM neural networks. In this framework, three types of features are first extracted, including CNN features with pre-trained networks, histograms of oriented gradients and color histograms. Second, those low-level features are concatenated and taken as the inputs to an autoencoder version of the ELM for deep feature-level fusion. Third, we propose an ensemble of adaptive ELMs for decision-level fusion using the previously obtained feature-level fusion representations. Extensive experiments are conducted on an up-to-date large-scale clothing image data set. Those experimental results show that the proposed framework is competitive and efficient.
To achieve different products distributions and improve energy conversion performance for the coal utilization with zero carbon emission, an advanced polygeneration system incorporating the methanol ...synthesis and Allam power cycle (an oxy-combustion, direct-fired supercritical carbon dioxide Brayton cycle) is proposed. The water-gas shift reaction is combined with the syngas recycle to obtain different products (i.e., methanol and electric power) distributions. Moreover, different from the conventional coal-to-methanol process, the carbon dioxide generated by the shift reaction is not separated from the shifted syngas but enters the Allam cycle, which can be simply split from the cooled exhaust without additional energy penalty. In other words, the carbon dioxide separation energy penalty of the methanol production is reduced to zero by connecting the coal-to-methanol process and the Allam cycle. In this study, the material flow and energy conversion characteristics of the proposed polygeneration system is revealed. The influences of the carbon monoxide shift ratio and syngas recycle ratio on the methanol productivity, net electric power throughput and the corresponding fuel saving ratio are comprehensively analyzed. The optimum strategy of the combination of the water-gas shift reaction and syngas recycle is obtained with the methanol/electricity energy ratio ranging from 0.75 to 2.71; and the highest fuel saving ratio soars to 5.79% as the methanol/electricity ratio is 1.55 at the carbon monoxide shift ratio of 0.17 and the syngas recycle ratio of 0.8.
•Water-gas shift reaction is combined with syngas recycle.•Shift-generated CO2 enters the Allam cycle for carbon capture.•Optimum combination strategy is obtained for different products distributions.•Highest fuel saving ratio is 5.79% with the methanol/electricity ratio of 1.55.
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