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The domain of Aspect-based Sentiment Analysis, in which aspects are extracted, their sentiments are analyzed and sentiments are evolved over time, is getting much attention with increasing feedback of public and customers on social media. The immense advancements in the field urged researchers to devise new techniques and approaches, each sermonizing a different research analysis/question, that cope with upcoming issues and complex scenarios of Aspect-based Sentiment Analysis. Therefore, this survey emphasized on the issues and challenges that are related to extraction of different aspects and their relevant sentiments, relational mapping between aspects, interactions, dependencies and contextual-semantic relationships between different data objects for improved sentiment accuracy, and prediction of sentiment evolution dynamicity. A rigorous overview of the recent progress is summarized based on whether they contributed towards highlighting and mitigating the issue of Aspect Extraction, Aspect Sentiment Analysis or Sentiment Evolution. The reported performance for each scrutinized study of Aspect Extraction and Aspect Sentiment Analysis is also given, showing the quantitative evaluation of the proposed approach. Future research directions are proposed and discussed, by critically analysing the presented recent solutions, that will be helpful for researchers and beneficial for improving sentiment classification at aspect-level.

The nitrogen-containing aromatic compounds found in the petrochemical industry are varied and extend beyond classes such as the anilines, pyrroles and pyridines. Quantification of these nitrogen-containing compounds that may occur in complex mixtures has practical application for quality assurance, process development and the evaluation of conversion processes. Selective detection of nitrogen-containing species in complex mixtures is possible by making use of gas chromatography coupled with a nitrogen phosphorous detector (GC-NPD), which is also called a thermionic detector. Despite the linearity of the NPD response to individual nitrogen-containing compounds, the response factor is different for different compounds and even isomers of the same species. Quantitative analysis using an NPD requires species-specific calibration. The reason for the sensitivity of the NPD to structure is related to the ease of forming the cyano-radical that is ionized to the cyanide anion, which is detected. The operation of the NPD was related to the processes of pyrolysis and subsequent ionization. It was possible to offer plausible explanations for differences in response factors for isomers based on pyrolysis chemistry. Due to this relationship, the NPD response can in the same way be used to provide information of practical relevance beyond its analytical value and a few possible applications were outlined.

Hydrogen economy is one of the most promising candidates to replace the current energy system on depleting fossil fuels. As a clean and sustainable way to produce hydrogen, electrocatalytic water splitting attracts ever-increasing interest from the research community. Although the wide application of platinum group metal (PGM) catalysts is limited because of the scarcity and high cost toward hydrogen evolution reaction (HER), the non-PGM electrocatalysts usually suffer from unsatisfactory activity and poor durability. In this work, we report an active and durable V-doped Ni5P4 electrocatalyst that can be used for all-pH HER. Particularly, V-Ni5P4 has an HER activity that is comparable to that of Pt in preferred alkaline media, with overpotentials as low as 13 mV and 295 mV at current densities of 10 and 1000 mA cm-2, respectively. The low-cost V-Ni5P4 that enables ultrahigh current density (i.e., at the level of A cm-2) would be of great interest to the hydrogen production industry.

Electrocatalytic water splitting offers a sustainable route for hydrogen production, enabling the clean and renewable alternative energy system of hydrogen economy. The scarcity and high-cost of platinum-group-metal (PGM) materials urge the exploration of high-performance non-PGM electrocatalysts. Herein, a unique hierarchical structure of Ni/V2O3 with extraordinary electrocatalytic performance (e.g., overpotentials as low as 22 mV at 20 mA·cm−2 and 94 mV at 100 mA·cm−2) toward hydrogen evolution reaction in alkaline electrolyte (1 M KOH) is reported. The investigation on the hierarchical Ni/V2O3 with a bimodal size-distribution also offers insight of interfacial engineering that only proper Ni/V2O3 interface can effectively improve H2O adsorption, H2O dissociation as well as H adsorption, for an efficient hydrogen production.

A series of deep learning approaches extract a large number of credibility features to detect fake news on the Internet. However, these extracted features still suffer from many irrelevant and noisy features that restrict severely the performance of the approaches. In this paper, we propose a novel model based on Adversarial Networks and inspirited by the Shared-Private model (ANSP), which aims at reducing common, irrelevant features from the extracted features for information credibility evaluation. Specifically, ANSP involves two tasks: one is to prevent the binary classification of true and false information for capturing common features relying on adversarial networks guided by reinforcement learning. Another extracts credibility features (henceforth, private features) from multiple types of credibility information and compares with the common features through two strategies, i.e., orthogonality constraints and KL-divergence for making the private features more differential. Experiments first on two six-label LIAR and Weibo datasets demonstrate that ANSP achieves the state-of-the-art performance, boosting the accuracy by 2.1%, 3.1%, respectively and then on four-label Twitter16 validate the robustness of the model with 1.8% performance improvements., Comment: Information Sciences

This paper addresses the problem of inferring link loss rates based on network performance tomography in noisy network systems. Since network tomography emerged, all existing tomography-based methods are limited to the fulfillment of a basic condition: both network topologies and end-to-end routes must be absolutely accurate, which in most cases is impractical, especially for large-scale heterogeneous networks. To overcome the impracticability of tomography-based methods, we propose a robust tomography-based loss inference method capable of accurately inferring all link loss rates even when the given knowledge about the system is unreliable. Rather than computing the link loss rates directly from end-to-end loss rates, it calculates an upper bound for each link loss rate. It then infers all the link loss rates that most closely conform to the measurement results within their upper bounds. For a scenario where noisy measurements are caused by link (or router port) failures, we propose a hidden fault diagnosis approach that utilizes the inferred link loss rates to pinpoint the insidious faults that are hardly detected. It first determines the possible fake routes based on inferred link loss rates. Subsequently, it finds the maximum probable faults that can best explain the fake routes. Through intensive experiments, the results strongly confirm the promising performance of our proposed approaches.

In this paper, a novel image splicing detection and localization scheme is proposed based on the local feature descriptor which is learned by deep convolutional neural network (CNN). A two-branch CNN, which serves as an expressive local descriptor is presented and applied to automatically learn hierarchical representations from the input RGB color or grayscale test images. The first layer of the proposed CNN model is used to suppress the effects of image contents and extract the diverse and expressive residual features, which is deliberately designed for image splicing detection applications. In specific, the kernels of the first convolutional layer are initialized with an optimized combination of the 30 linear high-pass filters used in calculation of residual maps in spatial rich model (SRM), and is fine-tuned through a constrained learning strategy to retain the high-pass filtering properties for the learned kernels. Both the contrastive loss and cross entropy loss are utilized to jointly improve the generalization ability of the proposed CNN model. With the block-wise dense features for a test image extracted by the pre-trained CNN-based local descriptor, an effective feature fusion strategy, known as block pooling, is adopted to obtain the final discriminative features for image splicing detection with SVM. Based on the pre-trained CNN model, an image splicing localization scheme is further developed by incorporating the fully connected conditional random field (CRF). Extensive experimental results on several public datasets show that the proposed CNN based scheme outperforms some state-of-the-art methods not only in image splicing detection and localization performance, but also in robustness against JPEG compression.

Traditional prediction methods of legal judgment rarely work well on few-shot charge prediction task, which is intended to predict possible crimes, laws and terms according to a given few-shot case description. A major challenge in developing an efficient few-shot charge prediction algorithm is how to effectively use all available features contained in the typical cases. To this end, we proposed a novel few-shot charge prediction method based on deep learning framework, named Semi-supervised Learning with BERT-text-Convolutional neural network (i.e. SLBCNN), which utilizes the Attribute-Tri-Training method to extract the features from the few-shot case data. Experiments on three datasets of Criminal-S, Criminal-M, and Criminal-L demonstrate that SLBCNN achieves the state-of-the-art performance, boosting the F1 by 5.2%, 4.2%, and 1.5%, respectively.

With the development of IOT and 5G technology, people’s demand for information acquisition is more inclined to accuracy, intelligence and timeliness. How to help designer obtain the real-time information of specific product reviews from the massive online consumers and upgrade the new design strategy has become a hot topic for research. In this paper, we define the problem as an aspect extraction task, and propose a novel deep learning model that comprises of three modules: pre-training language model embedding, multi-scale transmission network and contextual summary, which aims to provide an end-to-end solution without any additional supervision. To this end, we adopt BERT to overcome the disadvantage of traditional embedding methods, which cannot combine contextual information. Multi-scale transmission network is proposed to integrate the Bi-GRU and a group of CNN networks to extract sequential and local features of words respectively. Contextual summary is a tailor-made representation distilled from the input sentence, conditioned on each current word, and thus can assist aspect prediction. Experimental results over three benchmark SemEval datasets clearly illustrate that our model can achieve the state-of-the-art performance.

How to classify incredible messages has attracted great attention from academic and industry nowadays. The recent work mainly focuses on one type of incredible messages (a.k.a rumors or fake news) and achieves some success to detect them. The existing problem is that incredible messages have different types on social media, and rumors or fake news cannot represent all incredible messages. Based on this, in the paper, we divide messages on social media into five types based on three dimensions of information evaluation metrics. And a novel method is proposed based on deep learning for classifying the five types of incredible messages on social media. More specifically, we use attention mechanism to obtain deep text semantic features and strengthen emotional semantics features, meanwhile, construct universal metadata as auxiliary features, concatenating them for incredible messages classification. A series of experiments on two representative real-world datasets demonstrate that the proposed method outperforms the state-of-the-art methods.

The separation of solution gas has great influence on the development of gas-bearing tight oil reservoirs. In this study, physical simulation and high-pressure mercury intrusion were used to establish a method for determining the porous flow resistance gradient of gas-bearing tight oil reservoirs. A mathematical model suitable for injection–production well networks is established based on the streamline integral method. The concept of pseudo-bubble point pressure is proposed. The experimental results show that as the back pressure decreases from above the bubble point pressure to below the bubble point pressure, the solution gas separates out. During this process, the porous flow resistance gradient is initially equal to the threshold pressure gradient of the oil single-phase fluid, then it becomes relatively small and stable, and finally it increases rapidly and exponentially. The lower the permeability, the higher the pseudo-bubble point pressure, and the higher the resistance gradient under the same back pressure. For tight reservoirs, the production pressure should be maintained above the pseudo-bubble point pressure when the permeability is lower than a certain value. When the permeability is higher than a certain value, the pressure can be reduced below the pseudo-bubble point pressure, and there is a reasonable range. The mathematical results show that after degassing, the oil production rate and the effective utilization coefficient of oil wells decline rapidly. These declines occur later and have a flat trend for high permeability formations, and the production well pressure can be reduced to a lower level. Fracturing can effectively increase the oil production rate after degassing. A formation that cannot be utilized before fracturing because of the blocked throats due to the separation of the solution gas can also be utilized after fracturing. When the production well pressure is lower than the bubble point pressure, which is not too large, the fracturing effect is better.

Due to the complex environments in real fields, it is challenging to conduct identification modeling and diagnosis of plant leaf diseases by directly utilizing in-situ images from the system of agricultural Internet of things. To overcome this shortcoming, one approach, based on small sample size and deep convolutional neural network, was proposed for conducting the recognition of cucumber leaf diseases under field conditions. One two-stage segmentation method was presented to acquire the lesion images by extracting the disease spots from cucumber leaves. Subsequently, after implementing rotation and translation, the lesion images were fed into the activation reconstruction generative adversarial networks for data augmentation to generate new training samples. Finally, to improve the identification accuracy of cucumber leaf diseases, we proposed dilated and inception convolutional neural network that was trained using the generated training samples. Experimental results showed that the proposed approach achieved the average identification accuracy of 96.11% and 90.67% when implemented on the data sets of lesion and raw field diseased leaf images with three different diseases of anthracnose, downy mildew, and powdery mildew, significantly outperforming those existing counterparts, indicating that it offered good potential of serving field application of agricultural Internet of things.

In order to obtain more accurate pose estimation, the visual-inertial odometry (VIO) system with outlier rejection and loop closure is proposed in this paper. Considering that feature matching is an important part in the front-end of the VIO system, its accuracy will affect the performance of the entire system. So we introduce an outlier rejection method of the grid-based motion statistics (GMS) algorithm to the VIO system. And for more robust feature correspondence and better camera pose estimation, we propose an improved GMS method to eliminate the mismatched points. Besides, we adopt the loop closure strategy to correct the cumulative error of the VIO system. Finally, we estimate the camera pose, velocity and IMU bias simultaneously by minimizing the loss function which contains reprojection error and IMU error. A large number of experiments on EuRoC demonstrate that the proposed method outperforms the advanced VIO system ROVIO and is comparable to the state-of-the-art VIO system OKVIS.

Due to the explosive growth of image data, image annotation has been one of the most popular research directions in computer vision. It has been widely used in image retrieval, image analysis and understanding. Because traditional manual image annotation is time consuming, more advanced automatic annotation methods are needed. A major challenge in developing an efficient image annotation method is how to effectively use all available information contained in the data. To this end, this paper proposes a novel image annotation framework that uses multiple information from data. It employs nonnegative matrix tri-factorization (NMTF) to simultaneously factorize image-to-label, image-to-feature, and feature-to-label relation matrices using their intertype relationships and incorporates the intratype information through manifold regularizations. This method can be referred to as the trigraph regularized collective matrix tri-factorization framework (TG-CMTF). TG-CMTF captures the correlations among different labels, different images and different features. By taking advantage of these relations from images, features and labels, TG-CMTF can achieve better annotation performance than most state-of-the-art methods. The promising experimental results on three standard benchmarks have shown the effectiveness of this information. Furthermore, we show the annotation process as a precise optimization problem and solve it by an iterative algorithm, which proves the correctness of the proposed method from the mathematical theory.

Spinal cord injury (SCI) remains a challenging clinical problem worldwide, due to the lack of effective drugs for precise treatment. Among the complex pathophysiological events following SCI, reactive oxygen species (ROS) overproduction plays a particularly significant role. As therapeutic agents for neurological diseases, tetramethylpyrazine (TMP) and monosialotetrahexosylganglioside (GM1) have been widely used in the clinical treatment of SCI. Our previous studies have reported that functionalized selenium nanoparticles (SeNPs) exhibit excellent antioxidant activity against oxidative stress-related diseases. Therefore, in this study, novel multifunctionalized SeNPs decorated with polysaccharide-protein complex (PTW)/PG-6 peptide and loaded with TMP/GM1 were rationally designed and synthesized, which exhibited a satisfactory size distribution and superior stability. Furthermore, the protective effects of SeNPs@GM1/TMP on PC12 cells against tert-butyl hydroperoxide (t-BOOH)-induced cytotoxicity and the underlying mechanisms were also explored. Flow cytometric analysis indicated that SeNPs@GM1/TMP showed strongly protective effects against t-BOOH-induced G2/M phase arrest and apoptosis. Moreover, we found that SeNPs@GM1/TMP could attenuate ROS overproduction to prevent mitochondria dysfunction via inhibiting the activation of p53 and MAPK pathways. Effects of SeNPs@GM1/TMP on functional recovery after SCI were evaluated by the Basso-Beattie-Bresnahan (BBB) locomotion scale, inclined plane test, and footprint analysis. The results of hematoxylin-eosin staining and Nissl staining also showed that SeNPs@GM1/TMP provided a neuroprotective effect in SCI rats. This finding suggests that SeNPs@GM1/TMP could be further developed as a promising nanomedicine for efficient SCI treatment.

Recently, many methods discover effective evidence from reliable sources by appropriate neural networks for explainable claim verification, which has been widely recognized. However, in these methods, the discovery process of evidence is nontransparent and unexplained. Simultaneously, the discovered evidence only roughly aims at the interpretability of the whole sequence of claims but insufficient to focus on the false parts of claims. In this paper, we propose a Decision Tree-based Co-Attention model (DTCA) to discover evidence for explainable claim verification. Specifically, we first construct Decision Tree-based Evidence model (DTE) to select comments with high credibility as evidence in a transparent and interpretable way. Then we design Co-attention Self-attention networks (CaSa) to make the selected evidence interact with claims, which is for 1) training DTE to determine the optimal decision thresholds and obtain more powerful evidence; and 2) utilizing the evidence to find the false parts in the claim. Experiments on two public datasets, RumourEval and PHEME, demonstrate that DTCA not only provides explanations for the results of claim verification but also achieves the state-of-the-art performance, boosting the F1-score by 3.11%, 2.41%, respectively., ACL 2020

The decomposition of water into hydrogen and oxygen is an effective method to generate new energy. How to develop highly efficient catalysts with low cost for the hydrolysis of water is a huge challenge. Herein, heterogeneous CDs/NiCo2S4/Ni3S2 nanorods were constructed on nickel foam (CDs/NiCo2S4/Ni3S2/NF) by Co ion exchange on carbon dots (CDs) and sulfur codoped nickel foam (NF). The designed CDs/NiCo2S4/Ni3S2/NF as a self-standing electrocatalyst shows excellent electrocatalytic properties. In the hydrogen evolution reaction process, the current density can reach 10 mA cm–2 only with the overpotential of 0.127 V. During the oxygen evolution reaction process, overpotential of 0.116 V is enough to achieve the same current density. Meanwhile, the CDs/NiCo2S4/Ni3S2/NF electrode can serve as both anode and cathode in alkaline electrolyte, and overpotential of 1.50 V can drive the overall water splitting with superior durability. Such excellent properties are mainly due to (1) a large number of exposed activ...

Synchronous contention-based medium access control (MAC) protocols play a vital role in the delay guarantee essentially required by many recent delay-sensitive applications. Great efforts have been made to improve the end-to-end delay by means of optimizing the medium access performance. However, their effectiveness is still constrained by the contradiction between reducing the collision probability and idle listening duration. Instead of traditional tradeoff strategies, developing a way to further alleviate the contradiction is definitely beneficial for further improving the medium access performance. To this end, a self-adaptive implicit contention window adjustment mechanism for SMAC (Sensor MAC) is proposed based on the investigation of the medium competition behavior impacting the medium access delay and delay jitter, from the perspective of any given contending node. We mathematically derive the expected medium access delay for revealing the strengths and weaknesses of typical dynamic contention window mechanisms. Moreover, it is discovered that their delay-oriented optimizing window only offers fairly limited improvement in the medium access performance since the uniformly random slot-selection mechanism results in high delay jitter and the contradiction between reducing the collision probability and idle listening duration. In order to overcome this drawback, furthermore, an optimization model of the node contention priority distribution is proposed to alleviate the aforementioned contradiction by means of optimizing slot-selection mechanism. Finally, a self-adaptive implicit contention window adjustment (SICA) mechanism is designed integrating delay-oriented optimizing contention window, contention priority optimization model with the estimation method of contenders' number. Through simulations in OMNeT++, the results demonstrate that the proposed SMAC-SICA outperforms its peers in terms of delay guarantee, energy efficiency and throughput.

Non-noble, high-efficient electrodes with excellent durability are desirable for water splitting. Herein, a facile strategy has been proposed to in-situ grow nitrogen-doped carbon dots (NCDs) decorated Ni3S2 on Ni foam (NCDs/Ni3S2/NF) by hydrothermal treatment of Ni foam in water solution of thiourea and NCDs. Interestingly, coral-like NCDs/Ni3S2/NF composites are unprecedentedly prepared without any templates, where the Ni3S2 nanorods mainly expose {021} high-index facets. The resultant NCDs/Ni3S2/NF composites are directly used as self-supported electrodes for overall water splitting which deliver a current density of 10 mA cm−2 at 1.50 V with excellent durability in 1.0 M KOH solution.

Considerable works have been devoted on developing high-efficiency nonplatinum electrocatalysts for hydrogen evolution reaction (HER). Herein, 3D heteromorphic NiCo2S4/Ni3S2 nanosheets network has been constructed on Ni foam (denoted as NiCo2S4/Ni3S2/NF) serving as a self-standing electrocatalyst through directly thermal sulfurization of a single-source NiCo-layered double hydroxide precursor. The resultant NiCo2S4/Ni3S2/NF electrode exhibits outstanding electrocatalytic HER performance with an extremely low onset overpotential of 15 mV and long-term durability in alkaline solution. Such enhanced HER performance can be credited to (1) the massive exposed active sites provided by mixed transition metal chalcogenides (NiCo2S4 and Ni3S2), (2) the strong interfacial interaction at NiCo2S4/Ni3S2 heterojunction interfaces with the strengthened H binding, and (3) the porous highly conductive Ni foam substrate with accelerated electron transfer. This work opens up a new direction to fabricate effective and non-no...

Detailed characterization of feed materials can assist with development of new technology and troubleshooting existing units. In this work a methodology for the compound specific analysis of oxygen-, sulfur-, and nitrogen-containing compounds in naphtha is presented. It was applied to the analysis of industrial thermally cracked naphtha produced from Athabasca oilsands bitumen, with 0.25 wt % O, 0.90 wt % S, and 0.09 wt % N. The main oxygen-containing compound classes were acyclic carboxylic acids, ketones, and phenols. Interestingly, the carboxylic acids were almost exclusively linear and branched species in the C3–C11 range. These compounds could be explained by a thermal cracking pathway involving ring-opening of one or more adjacent naphthenic rings. The main sulfur-containing compound class was cyclic thioethers, with a minor amount of thiols. No thiophenes were identified and identification of the most abundant compound, 2-methyl tetrahydrothiophene, was confirmed with an authentic compound. This su...

Recently, neural networks based on multi-task learning have achieved promising performance on fake news detection, which focus on learning shared features among tasks as complementary features to serve different tasks. However, in most of the existing approaches, the shared features are completely assigned to different tasks without selection, which may lead to some useless and even adverse features integrated into specific tasks. In this paper, we design a sifted multi-task learning method with a selected sharing layer for fake news detection. The selected sharing layer adopts gate mechanism and attention mechanism to filter and select shared feature flows between tasks. Experiments on two public and widely used competition datasets, i.e. RumourEval and PHEME, demonstrate that our proposed method achieves the state-of-the-art performance and boosts the F1-score by more than 0.87%, 1.31%, respectively., 10 pages, 5 figures, EMNLP 2019

Due to the limited energy budget, great efforts have been made to improve energy efficiency for wireless sensor networks. The advantage of compressed sensing is that it saves energy because of its sparse sampling, however, it suffers inherent shortcomings in relation to timely data acquisition. In contrast, prediction-based approaches are able to offer timely data acquisition, but the overhead of frequent model synchronization and data sampling weakens the gain in the data reduction. The integration of compressed sensing and prediction-based approaches is one promising data acquisition scheme for the suppression of data transmission, as well as timely collection of critical data, but it is challenging to adaptively and effectively conduct appropriate switching between the two aforementioned data gathering modes. Taking into account the characteristics of data gathering modes and monitored data, this research focuses on several key issues, such as integration framework, adaptive deviation tolerance, and adaptive switching mechanism of data gathering modes. In particular, the adaptive deviation tolerance is proposed for improving the flexibility of data acquisition scheme. The adaptive switching mechanism aims at overcoming the drawbacks in the traditional method that fails to effectively react to the phenomena change unless the sampling frequency is sufficiently high. Through experiments, it is demonstrated that the proposed scheme has good flexibility and scalability, and is capable of simultaneously achieving good energy efficiency and high-quality sensing of critical events.

In this paper, a novel image forgery detection and localization scheme is proposed based on deep convolutional neural network (CNN) which incorporates multi-semantic CRF-based attention model. The proposed method is based on the key observation that the boundary transition artifacts arising from the blending operations are ubiquitous in various image forgery manipulations, which is well characterized in our method with CRF (conditional random field) based attention model by generating attention maps to represent the probability of being forged for each pixel in the image. The resulting attention maps are then used to re-weight the convolutional feature maps for noise suppression and highlighting the informative regions surrounding the forged boundaries, guiding the network to capture more intrinsic features for image forgery rather than manipulation-specific artifacts. Multi-scale attention maps with various semantics are adopted to take full advantages of both the local and global information for improvement of generalization capability, which is then incorporated with a CNN model for effective image forgery detection and localization. Extensive experimental results on several public datasets show that the proposed scheme outperforms or rivals to other state-of-the-art methods in image forgery detection and localization.

It is necessary to carry out experiments on cores with different degrees of crack development when studying the seepage law of cracked reservoirs and evaluating cracks. The seepage experiment in the laboratory requires cores with different degrees of microcrack development; cores obtained via conventional drilling cannot meet the requirements, and the efficacies and evaluation methods of geological parameters used for artificial cracks are not perfect. In this study, cores are loaded using a triaxial gripper, and cracks are produced by changing the difference of stress; the relationship between the increased rate of permeability and the change in stress concentration is used to evaluate the degree of development of the crack in real time. The angle between the cracks and the maximum principal stress direction, calculated using the Mohr–Coulomb failure criterion, is 20–27.5°, which provides theoretical support for the process of crack creation. The experimental results show that the permeability variation curve shows two obvious turning points, which divide the whole zone into a reduction zone, a slow increase zone, and a rapid increase zone. Through the obtained experimental and evaluation results, a complete system for crack creation and evaluation is established, which can provide strong support for the study of cracked reservoirs.

Metal carbides with unique electrical properties and high catalytic efficiency have attracted tremendous interest for applications involving water electrolysis. In this paper the design of a novel cation modulating electrocatalyst Ni3ZnC0.7 using homogeneous bimetallic metal–organic frameworks (NiZn-MOFs) as precursor is reported. The synergistic effect between the unique chemical properties and nanostructure endows Ni3ZnC0.7 with a remarkable electrocatalytic performance. The balance of Ni(0) and Ni(II) achieved by Zn modulation is beneficial to both the hydrogen evolution reaction and the oxygen evolution reaction under alkaline conditions. The bifunctional catalyst, Ni3ZnC0.7, can drive overall water splitting through a symmetrical double electrode at a current density of 10 mA cm−2 with only 1.65 mV, and also shows excellent stability without obvious degradation after 24 h of operation, which makes it a promising noble metal free electrocatalyst. This easy preparation method for Ni3ZnC0.7 makes it a promising candidate for a practical answer to water splitting. Furthermore, this cation modulating strategy provides an ingenious way to realize the precise control of the catalysts and can be extended to the synthesis of various novel nanomaterials.

The selective removal of nitrogen-containing compounds from oil and oil fractions is of interest because of the potential deleterious impact of such compounds on products and processes. Problems caused by nitrogen-containing compounds include gum formation, acid catalyst inhibition and deactivation, acid–base pair-related corrosion, and metal complexation. A brief overview of the classes of nitrogen compounds found in oil is provided. The review of processes to remove nitrogen from oil emphasizes studies that investigated denitrogenation of industrial feedstocks, such as refinery fractions, heavy oils, and bitumens. The main topics covered are hydrotreating, liquid–liquid phase partitioning, solvent deasphalting, adsorption, chemical conversion followed by separation, and microbial conversion. Chemical conversion processes include oxidative denitrogenation, N-alkylation, complexation with metal salts, and conversion in high-temperature water. There are many processes for denitrogenation by separation of t...

Designing economical and high-efficiency electrocatalysts for overall water splitting is urgently needed but remains a long and arduous task. Herein, we synthesized hydrotalcite-like Ni(OH)2 nanosheets growing on Ni foam (Ni(OH)2/NF) via a facile one-pot hydrothermal method. With the assistance of a rotating oven, Ni(OH)2 nanosheets demonstrate a regular hexagonal morphology and homogeneous distribution. The resultant Ni(OH)2/NF electrode shows superior electrocatalytic activity and durability for the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER), as well as the overall water splitting. The Ni(OH)2/NF electrode delivers 20 mA·cm–2 at an overpotential of 172 mV for HER, 50 mA·cm–2 at an overpotential of 330 mV for OER, and 10 mA·cm–2 at a cell voltage of 1.68 V for water electrolysis in 1.0 M KOH. The present study demonstrates a feasible and effective strategy to prepare highly efficient electrocatalysts for water electrolysis.

Porous carbon spheres (PCSs) with high uptakes for CO2, CH4, and H2 were prepared from sustainable starch-derived hydrochar via a simple CO2 activation without any template. The PCSs show regularly-spherical morphology, large surface area (3350 m2/g), and high pore volume (1.75 ml/g), which have been rarely reported for physically-activated carbons. Owing to the textural properties, their uptakes at 20 bar for CO2 (21.2 mmol/g, 298 K), CH4 (10.7 mmol/g, 298 K), and H2 (6.4 wt%, 77 K) all rank among the highest values ever reported for activated carbons, and the CH4 uptake is the highest one for activated carbons. Additionally, the adsorption and desorption of the aforementioned gases onto the material are reversible, indicating that the PCSs can be regenerated readily by pressure swing adsorption. Given the sustainability and environmental friendliness of the starch-derived feedstock, low costs of physical activation, large uptakes for multiple gases, and excellent adsorption reversibility, the obtained PCSs are promising candidates for storage of multiple energy-source-related gases.

Hf–Al intermetallics are novel promising high-temperature structural materials, which exhibit excellent mechanical properties. In this work, the structural parameters, elastic constants, and mechanical anisotropies of Hf–Al intermetallics are investigated by applying the latest meta-GGA functional SCAN as well as several other widely used density functionals such as LDA, PBE and PBEsol. It is found that HfAl2 is the most thermodynamically stable stoichiometric compound among all Hf–Al binary intermetallics. The elastic moduli like B, G, E, ductility index (B/G ratio) and Poisson's ratio (σ) of Hf–Al intermetallics are predicted based on Voigt–Reuss–Hill approximation, indicating most Hf–Al binary intermetallics are brittle except Hf5Al3 and Hf2Al. Besides, the three-dimensional (3D) contour plots of mechanical moduli are provided to characterize the elastic anisotropy in Hf–Al intermetallics. HfAl shows obvious anisotropy in both Young's and bulk moduli along different crystallographic directions, while very weak anisotropy is revealed for both HfAl2 and Hf4Al3 phases since the 3-D surface contours of mechanical moduli show little deviations from isotropic spherical shapes. The calculated electronic properties indicate that relatively high thermodynamic stability of HfAl2 phase is mainly ascribed to the formation of a pseudo-gap in the electron density of states originating mainly from the Al-2p and Hf-5d states at the Fermi level.

Intensive animal husbandry is becoming more and more popular with the adoption of modern livestock farming technologies. In such circumstances, it is required that the welfare of animals be continuously monitored in a real-time way. To this end, this study describes one on-farm welfare monitoring system for goats, with a combination of Internet of Things and machine learning. First, the system was designed for uninterruptedly monitoring goat growth in a multifaceted and multilevel manner, by means of collecting on-farm videos and representative environmental data. Second, the monitoring hardware and software systems were presented in detail, aiming at supporting remote operation and maintenance, and convenience for further development. Third, several key approaches were put forward, including goat behavior analysis, anomaly data detection, and processing based on machine learning. Through practical deployment in the real situation, it was demonstrated that the developed system performed well and had good potential for offering real-time monitoring service for goats’ welfare, with the help of accurate environmental data and analysis of goat behavior.

Currently, the state-of-the-art method about object detector in image mainly depends on deep backbones, such as ResNet-50, DarkNet-53, ResNet-101 and DenseNet-169, which benefits for their powerful capability of feature representations but suffers from high computational cost. On the basis of fast lightweight backbone network (i.e., VGG-16), this paper improves the capability of feature representations by combining features of different receptive fields and cross-fusing feature pyramids, and finally establishes a fast and accurate detector. The architecture of our model is designed to integrate FC-CF Net with two sub-modules: FC module and CF module. Inspired by the structure of receptive fields in visual systems of human, we propose a novel method about Feature Combination Based on Receptive Fields module (FC module), which takes the relationship between the size and eccentricity of receptive fields into account, and then combine them with original features for increasing the receptive field and information of the feature map. Furthermore, based on the structure of FPN (Feature Pyramid Network), we design a novel Cross-Fusion Feature Pyramid module (CF module), which combines top-down and bottom-up connections to fuse features across scales, and achieves high-level semantic feature map at all scales. Extensive experiments on PASCAL VOC 2007 and 2012 demonstrate that FC-CF Net achieves state-of-the-art detection accuracy (i.e. 82.4% mAP, 80.5% mAP) with high efficiency (i.e. 69 FPS, 35 FPS).

There is not only low-cost, easy-access, real-time and valuable information on social media, but also a large amount of false information. False information causes great harm to individuals, the society and the country. So how to detect false information? In the paper, we analyze false information further. We rationally select three information evaluation metrics to distinguish false information. We pioneer the division of information into 5 types and introduce them in detail from the definition, the focus, features, etc. Moreover, in this work, we propose a hybrid deep model to represent text semantics of information with context and capture sentiment semantics features for false information detection. Finally, we apply the model to a benchmark dataset and a Weibo dataset, which shows the model is well-performed.

Sentiment computing brings some new application opportunities and technique challenges in artificial intelligence of the next generation, and it has become a fascinating research field. In this paper, the conception of sentiment computing with some core elements and feature vectors is defined, and some vital issues are proposed. Based on the theories mentioned above, the subjective content or objective content is classified by some special algorithms in the scenarios of single modal, such as text, image, audio and video data. Furthermore, how to merge these different kinds of data and to form the multimodal analysis methods for emotion detection is an important problem, and the fusion strategy is summarized in the paper. Finally, some trends about the sentiment cognition and sentiment generation are analyzed, which provides new ways for further research work.

In order to solve a series of problems about IT service management in Enterprise, such as lower efficiency and service quality, complexed business processes and communication patterns, huge static management data and dynamic control data, etc., an intelligence-awareness IT service management platform, which is based on the Cyber Physical System (CPS) for constructing smart Industry 4.0, is proposed to optimize the management and service process for IT resources in this paper. The whole IT resources of enterprise mainly include software, hardware and employee resources. How to aware and integrate all kinds of IT resources with intelligence method together for analyzing and providing better service is a big challenges, especially, the physical state-awareness about all hardware and network facilities, the business process-awareness about management software and business data, the knowledge-awareness and service among the employees in Enterprise. Based on these critical IT resources, a new smart ITSM platform is designed and programed, which is not only use CPS meta-model to build the state-awareness smart environment for IT service, but also integrate ITIL (Information Technology Infrastructure Library) meta-model as a standard management process to aware the change about the process and knowledge library. The application results show that the new CPS-Awareness ITSM platform promotes the efficiency and qualify of IT service and provides early-alarm in the management process of the whole life cycle of IT resources in enterprise.

Some frame components, such as SYNC (frame synchronization) and RTS/CTS (Ready to Send/Clear to Send), are not taken into consideration when the traditional setting strategies conduct the optimization of SMAC (Sensor MAC) contention window size. This paper proposes mathematical models that allow the analysis of data packets forwarding delay within one SMAC virtual cluster. Simulation results in OMNeT++ show good agreements with the proposed mathematical models, validating the models' correctness. The curve analyses of the models confirm the existence of delay-optimization-oriented contention window size that is closely related to the number of simultaneously contending nodes. Afterwards, it is shown that SYNC, RTS/CTS and EIFS (Extended InterFrame Space) have impacts on the optimal contention window size and expected delivery delay to various degrees, as well as throughput and energy efficiency. One ideal setting strategy of delay-optimization-oriented SMAC contention window size requires the combination of the network scale, SYNC, RTS/CTS and EIFS. Additionally, it is demonstrated that the proposed setting strategy makes contributions to the improvement in the existing SMAC extensions when they are integrated with each other, in terms of the end-to-end delay, throughput and energy consumption.

With the development of a variety of image editing tools, performing digital image forgery and concealing the forgery edge is easier. On the other hand, these visually convincing tampering operations also make authentication of digital images difficult. Therefore, developing a precise and robust method to detect these splicing images is urgently required. In the past, some researchers proposed some methods, which achieves an accuracy of over 97%, but robustness of these methods remains unknown. In this paper, a novel image forgery detection method based on a special blocking strategy is proposed, in which the processing unit for each block is a rich model convolutional neural network (rCNN). The proposed method is not only able to detect the splicing image but also reserves its effectiveness under circumstances of JPG compression. Extensive experiments with CASIA v1.0, CASIO v2.0 and Columbia image forgery evaluation databases were carried out, which demonstrates the effectiveness and strong robustness of the proposed method.

In this paper, we present a new image forgery detection method based on deep learning technique, which utilizes a convolutional neural network (CNN) to automatically learn hierarchical representations from the input RGB color images. The proposed CNN is specifically designed for image splicing and copy-move detection applications. Rather than a random strategy, the weights at the first layer of our network are initialized with the basic high-pass filter set used in calculation of residual maps in spatial rich model (SRM), which serves as a regularizer to efficiently suppress the effect of image contents and capture the subtle artifacts introduced by the tampering operations. The pre-trained CNN is used as patch descriptor to extract dense features from the test images, and a feature fusion technique is then explored to obtain the final discriminative features for SVM classification. The experimental results on several public datasets show that the proposed CNN based model outperforms some state-of-the-art methods.

In order to forecast the price movement of stock with the correlated news events, an enhanced Topic-driven model with the positional weight of feature words and label of stocks, named LP-LDA model, is proposed to represent and analyze the intrinsic mechanism in financial market. The experiment results show that LP-LDA has a better performance than traditional LDA model. Especially, when the number of topics are increasing, the running time of LP-LDA model are 0.69s, 0.78 s and 1.15s at 100, 200 and 300 topics, respectively, which are better than LDA. Furthermore, Degree of Influence (DoI) is defined to describe the considerable influence about the news events on the price movement of certain stock, which provides a new mechanism to measure the fluctuating price. The experiment results shown that the coefficient of correlation between news topic and return rate of stock is 0.9137, which is much higher than other results of experiment.

In this study, we address the problem of selecting the optimal end-to-end paths for link loss inference in order to improve the performance of network tomography applications, which infer the link loss rates from the path loss rates. Measuring the path loss rates using end-to-end probing packets may incur additional traffic overheads for networks, so it is important to select the minimum path set carefully while maximizing their performance. The usual approach is to select the maximum independent paths from the candidates simultaneously, while the other paths can be replaced by linear combinations of them. However, this approach ignores the fact that many paths always exist that do not lose any packets, and thus it is easy to determine that all of the links of these paths also have 0 loss rates. Not considering these good paths will inevitably lead to inefficiency and high probing costs. Thus, we propose an adaptive path selection method that selects paths sequentially based on the loss rates of previously selected paths. We also propose a theorem as well as a graph construction and decomposition approach to efficiently find the most valuable path during each round of selection. Our new method significantly outperforms the classical path selection method based on simulations in terms of the probing cost, number of accurate links determined, and the running speed.