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Abstract This paper presents an interesting case study on Legacy Data Integration (LDI for short) for a Regional Cloud Arbitration Court. Due to the inconsistent structure and presentation, legacy arbitration cases can hardly integrate into the Cloud Court unless processed manually. In this study, we propose an AI-enabled LDI method to replace the costly manual approach and ensure privacy protection during the process. We trained AI models to replace tasks such as reading and understanding legacy cases, removing privacy information, composing new case records, and inputting them through the system interfaces. Our approach employs Optical Character Recognition (OCR), text classification, and Named Entity Recognition (NER) to transform legacy data into a system format. We applied our method to a Cloud Arbitration Court in Liaoning Province, China, and achieved a comparable privacy filtering effect while retaining the maximum amount of information. Our method demonstrated similar effectiveness as the manual LDI, but with greater efficiency, saving 90% of the workforce and achieving a 60%-70% information extraction rate compared to manual work. With the increasing development of informationalization and intelligentization in judgment and arbitration, many courts are adopting ABC technologies, namely Artificial intelligence, Big data, and Cloud computing, to build the court system. Our method provides a practical reference for integrating legal data into the system.

The recent few decades witnessed a quick growth in our knowledge in actinoid chemistry, particularly in actinoids’ behaviors in catalysis and biologic systems [...]

Self-supervised learning is a new machine learning method that does not rely on manually labeled data, and learns from rich unlabeled data itself by designing agent tasks using the input data as supervision to obtain a more generalized representation for application in downstream tasks. However, the current self-supervised learning suffers from the problem of relying on the selection and number of negative samples and the problem of sample bias phenomenon after graph data augmentation. In this paper, we investigate the above problems and propose a corresponding solution, proposing a graph contrastive learning algorithm without negative samples. The model uses matrix sketching in the implicit space for feature augmentation to reduce sample bias and iteratively trains the mutual correlation matrix of two viewpoints by drawing closer to the distance of the constant matrix as the objective function. This method does not require techniques such as negative samples, gradient stopping, and momentum updating to prevent self-supervised model collapse. This method is compared with 10 graph representation learning algorithms on four datasets for node classification tasks, and the experimental results show that the algorithm proposed in this paper achieves good results.

In real-world scenarios, obtaining the relationships between nodes is often challenging, resulting in incomplete network topology. This limitation significantly reduces the applicability of community detection methods, particularly neighborhood aggregation-based approaches, on structurally incomplete networks. Therefore, in this situation, it is crucial to obtain meaningful community information from the limited network structure. To address this challenge, the LPGSE algorithm was designed and implemented, which includes four parts: link prediction, structure observation, network estimation, and community partitioning. LPGSE demonstrated its performance in community detection in structurally incomplete networks with 10% missing edges on multiple datasets. Compared with traditional community detection algorithms, LPGSE achieved improvements in NMI and ARI metrics of 1.5781% to 29.0780% and 0.4332% to 31.9820%, respectively. Compared with similar community detection algorithms for structurally incomplete networks, LPGSE also outperformed other algorithms on all datasets. In addition, different edge-missing ratio settings were also attempted, and the performance of different algorithms in these situations was compared and analyzed. The results showed that the algorithm can still maintain high accuracy and stability in community detection across different edge-missing ratios.

Nowadays, the distributed tree-based index has been widely adopted to process queries on large-scale data. Index placement is an essential part of distributed tree-based index management. Current research on index placement relies on predefined rules, such as user-supplied functions, load balance, and data location. They ignore the distance between the index position and the query data (denoted as locality), which significantly determines the index placement efficiency. However, the locality relies on several factors related to unpredictable queries, and it is difficult to consider all of the factors. Measuring query performance can comprehensively consider the above query-related factors. Therefore, we propose a novel method that uses deep reinforcement learning (DRL) to solve the problem and adopts the query performance as evaluation feedback in DRL. Based on this idea, we design a tool called LiPTool that learns the optimal index placement. Specifically, LiPTool employs deep reinforcement learning (DRL) to find the response server for every query, maximizing the average query performance of the workload. Then, LiPTool builds the index on each server according to the corresponding queries. Finally, LiPTool gives the suggestion of index placement. Moreover, this paper compares LiPTool with the other two methods on multiple datasets, and the results show that LiPTool doubles the query performance. We believe that LiPTool is a good solution for dynamically adjusting index placement in autonomous database management.

Oxidative dehydrogenation of alkanes over boron nitride catalysts provides a new opportunity for efficient olefin production. Here, the authors discover in situ formed olefins can promote parent alkane conversion, and achieve activation of ethane by propane-derived olefins at a lower temperature.

The North China Plain plays a pivotal role in China’s crop production, contributing to 30% of the maize yield. Nevertheless, summer maize in this region faces challenges due to climatic constraints characterized by concurrent high temperatures and rainfall during the growing season, resulting in a relatively high evapotranspiration rate. In this study, we explored eight soil moisture-based threshold irrigation strategies, consisting of two upper limits and four lower limits, along with a rainfed mode (E). The upper and lower irrigation limits are expressed as a percentage of the field’s water-holding capacity (sfc). For the four full irrigation modes (A1, A2, A3, A4), the lower limits were set at 0.6 sfc, 0.6 sfc, 0.5 sfc, and 0.5 sfc, respectively. The upper limits were defined at two levels: 0.8 sfc for A1 and A2 and sfc for A3 and A4. Similarly, for the four deficit irrigation modes (B1, B2, B3, B4), the lower limits were established at 0.4 sfc, 0.4 sfc, 0.3 sfc, and 0.3 sfc, respectively, with the upper limits set at two levels: 0.8 sfc for B1 and B2 and the full sfc for B3 and B4. To investigate the impact of rainfall and potential evapotranspiration on these irrigation modes under long-term fluctuations, we employed a stochastic framework that probabilistically linked rainfall events and irrigation applications. The Monte Carlo method was employed to simulate a long-term series (4000a) of rainfall parameters and evapotranspiration using 62 years of meteorological data from the Xinxiang region, situated in the southern part of the North China Plain. Results showed that the relative yield and net irrigation water requirement of summer maize decreased with decreasing irrigation lower limits. Additionally, the interannual variation of rainfall parameters and evapotranspiration during the growing season were remarkable, which led to the lowest relative yield of the rainfed mode (E) aligned with a larger interannual difference. According to the simulation results, mode A4 (irrigation lower limit equals 0.5 sfc, irrigation upper limit equals 0.8 sfc) could be adopted for adequate water resources. Conversely, mode B2 is more suitable for a lack of water resources.

Direct ascorbic acid fuel cells (DAAFCs) employ biocompatible ascorbic acid (AA) as fuel, allowing convenient storage, transportation, and fueling as well as avoiding fuel crossover. The AA oxidation reaction (AAOR) largely governs the performance of DAAFCs. However, AAOR electrocatalysts currently have low activity, and state-of-the-art ones are limited to carbon black. Herein, we report the synthesis of an unprecedented AAOR electrocatalyst comprising 3.9 ± 1.1 nm CeO2 nanoparticles evenly distributed on carbon black simply by the wet chemical precipitation of Ce(OH)3 and a subsequent heat treatment. The resultant CeO2/C shows a remarkable AAOR activity with a peak current density of 13.1 mA cm−2, which is 1.7 times of that of carbon black (7.67 mA cm−2). According to X-ray photoelectron spectroscopy (XPS), the surface Ce3+ of CeO2 appears to contribute to the AAOR activity. Furthermore, our density functional theory (DFT) calculation reveals that that the proton of the hydroxyl group of AA can easily migrate to the bridging O sites of CeO2, resulting in a faster AAOR with respect to the pristine carbon, -COOH, and -C=O sites of carbon. After an i-t test, CeO2/C loses 17.8% of its initial current density, which is much superior to that of carbon black. CeO2 can capture the electrons generated by the AAOR to protect the -COOH and -C=O sites from being reduced. Finally, DAAFCs fabricated with CeO2/C exhibit a remarkable power density of 41.3 mW cm−2, which is the highest among proton-exchange-membrane-based DAAFCs in the literature.

The separation of trivalent actinides and lanthanides is a key step in the sustainable development of nuclear energy, and it is currently mainly realized via liquid–liquid extraction techniques. The underlying mechanism is complicated and remains ambiguous, which hinders the further development of extraction. Herein, to better understand the mechanism of the extraction, the contributing factors for the extraction are discussed (specifically, the sulfur-donating ligand, Cyanex301) by combing molecular dynamics simulations and experiments. This work is expected to contribute to improve our systematic understanding on a molecular scale of the extraction of lanthanides and actinides, and to assist in the extensive studies on the design and optimization of novel ligands with improved performance.

In this paper, we propose an outlier-detection algorithm for detecting network traffic anomalies based on a clustering algorithm and an autoencoder model. The BIRCH clustering algorithm is employed as the pre-algorithm of the autoencoder to pre-classify datasets with complex data distribution characteristics, while the autoencoder model is used to detect outliers based on a threshold. The proposed BIRCH-Autoencoder (BAE) algorithm has been tested on four network security datasets, KDDCUP99, UNSW-NB15, CICIDS2017, and NSL-KDD, and compared with representative algorithms. The BAE algorithm achieved average F-scores of 96.160, 81.132, and 91.424 on the KDDCUP99, UNSW-NB15, and CICIDS2017 datasets, respectively. These experimental results demonstrate that the proposed approach can effectively and accurately detect anomalous data.

The direct discharge of untreated organic wastewater poses significant threats to the environment and to human health. To address these threats, electrocatalytic oxidation technology has emerged as a key solution for organic wastewater treatment. Building on research conducted over the past three years, this review highlights the considerable advantages of electrocatalytic oxidation technology in the context of organic wastewater treatment, with a particular emphasis on the application of metal oxide electrodes. The review also provides a summary of the primary methods used in the preparation of such electrodes. Subsequently, the applications of both single-metal-oxide electrodes and metal oxide composite electrodes in organic wastewater treatment are summarized. Finally, we discuss the future development of metal oxide electrodes.

Abstract Objectives The aim of this study was to evaluate the role of antiviral drugs in reducing the risk of developing severe illness in patients with moderate COVID-19 pneumonia. Methods This retrospective cohort study included 403 adult patients with moderate COVID-19 pneumonia who were admitted to Shenzhen Third People’s Hospital, China. The antiviral drugs arbidol, interferon alpha-1b, lopinavir–ritonavir and ribavirin were distributed to the patients for treatment. The primary endpoint of this study was the time to develop severe illness. Results Of the 462 patients admitted, 403 had moderate COVID-19 symptoms at hospital admission and were included in this study. 90 of the 403 (22.3%) patients progressed to severe illness. The use of arbidol was associated with a lower severity rate 3.5% compared to control group 30.5%, p-value

BackgroundPercutaneous coronary intervention (PCI) is the preferred treatment method for coronary artery diseases (CAD). This study aimed to evaluate the effectiveness and complications of the Guidezilla™ guide extension catheter I (GGEC I) in transradial coronary intervention (TRI).MethodsThis case series study included patients with CAD who underwent TRI using the GGEC I between August 2016 and January 2019 at the First Affiliated Hospital of Xi’an Jiaotong University.ResultsA total of 221 patients aged 65.1 ± 9.26 years were included. Coronary angiography results indicated that most patients (77.8%) had triple-vessel lesions, including 47.5% with chronic total occlusion (CTO). A total of 237 target lesions were treated, most being type C lesions (95.8%). The most common indication for GGEC I use was heavy calcification (67%), followed by extreme tortuosity (12.2%), extreme tortuosity and heavy calcification (10.9%), distally located lesion (4.5%), picking up the retrograde wire (3.2%), anomalous vessel origin (1.8%), and releasing the burr incarceration (0.4%). The mean operation time was 58 min, and the overall success rate was 94.1%. Four patients received a drug-coated balloon. No significant differences were found in operation time and success rate among the low (32) CAD groups based on SYNTAX score stratification (P > 0.05). Two subacute thrombosis cases each were reported perioperatively, during hospitalization, and at the 1-month follow-up.ConclusionThe GGEC I might have advantages for TRI and is unaffected by SYNTAX score stratification.

Electrochemical impedance spectroscopy (EIS) is an efficient and non-destructive test for analyzing the bioelectrochemical processes of microbial fuel cells (MFCs). The key factors limiting the output performance of an MFC can be identified by quantifying the contribution of its various internal parts to the total impedance. However, little attention has been paid to the measurement conditions and diagrammatic processes of the EIS for MFC. This review, starting with the analysis of admittance of bioelectrode, introduces conditions for the EIS measurement and summarizes the representative equivalent circuit plots for MFC. Despite the impedance from electron transfer and diffusion process, the effect of unnoticeable capacitance obtained from the Nyquist plot on MFCs performance is evaluated. Furthermore, given that distribution of relaxation times (DRT) is an emerging method for deconvoluting EIS data in the field of fuel cell, the application of DRT-analysis to MFC is reviewed here to get insight into bioelectrode reactions and monitor the biofilm formation. Generally, EIS measurement is expected to optimize the construction and compositions of MFCs to overcome the low power generation.

Dissolved oxygen (DO) plays an important role in the performance of biological wastewater treatment systems. This study investigated the effect of the DO concentration on nutrient removal performance and microbial community structure in side-stream activated sludge hydrolysis (SSH) and conventional anaerobic/anoxic/aerobic (A2O) processes. The results showed that the change in DO had little effect on the removal performance of chemical oxygen demand (COD), and the removal efficiencies were about 90% for both reactors. Compared with the high DO level (4.1–6.9 mg/L), the A2O and SSH reactors had better nitrogen removal performance at low (0.5–2.2 mg/L) and moderate (2.2–3.9 mg/L) DO levels, with ammonia (NH4+-N) removal efficiencies of 88–89% and 89–91%, respectively, and total nitrogen (TN) removal efficiencies of 74–76% and 75–81%, respectively. Directly reducing the DO concentration from high to low reduced the phosphate removal efficiencies of the A2O and SSH reactors from 80.2% and 86.2% to 63.1% and 70.6%, respectively, while re-elevating the DO concentration to moderate levels significantly improved the phosphate removal efficiencies to 94.6% and 96.0%, respectively. Compared to the A2O reactor, the SSH reactor had more stable and better nutrient removal performance under different DO conditions, partly due to the additional carbon sources produced through the sludge fermentation in the side-stream reactor. The decrease in the DO concentration resulted in a decrease in the relative abundance of Acinetobacter but an increase in the relative abundance of Competibacter, potentially leading to the deterioration in phosphorus removal.

Abstract The assessment of protein–ligand interactions is critical at early stage of drug discovery. Computational approaches for efficiently predicting such interactions facilitate drug development. Recently, methods based on deep learning, including structure- and sequence-based models, have achieved impressive performance on several different datasets. However, their application still suffers from a generalizability issue because of insufficient data, especially for structure based models, as well as a heterogeneity problem because of different label measurements and varying proteins across datasets. Here, we present an interpretable multi-task model to evaluate protein–ligand interaction (Multi-PLI). The model can run classification (binding or not) and regression (binding affinity) tasks concurrently by unifying different datasets. The model outperforms traditional docking and machine learning on both binary classification and regression tasks and achieves competitive results compared with some structure-based deep learning methods, even with the same training set size. Furthermore, combined with the proposed occlusion algorithm, the model can predict the important amino acids of proteins that are crucial for binding, thus providing a biological interpretation.

Forestry monitoring and high-voltage cable inspection demand on low-cost climbing robots. The proposed climbing robot has simple control and low cost, enabling loaded, which drives by a single motor. Based on the overrunning spring clutch mechanisms, two motions of holding and climbing are realized by one motor. A rope-driven gripper is for adaptive enveloping holding effectively and a thron wheel is used to attach the climbing surface and stable climbing. The design parameters of the overrunning spring clutch mechanism and the rope-driven gripper are determined. The prototype and experiment setup are built. The enveloping holding experiment is carried out to verify the holding stability and shape adaptability of the rope-driven gripper. The trunk and pipe climbing experiments verify the climbing performance of the climbing robot and its application prospects with a certain load. In the future, as a low-cost climbing robot, a camera or operating mechanism can be equipped for tasks.

Microemulsion flooding is a effective enhanced oil recovery for low permeability. In this paper, combined the advantage of sulphobetaine surfactant and gemini surfactant, a kind of gemini sulphobetaine surfactant (DPSB) was used to prepared microemulsion. The oil–water interfacial tension and adsorption for different concentration surfactant was measured to evaluate the DPSB property. The stability of microemulsion, the microemulsion volume percentage, solubilisation parameter and relative phase volume, microemulsion viscosity was also measured. The performance of this surfactant was further optimized by adding the additives. The relative permeability was determined to analyze the fluid flow characteristics, and the recovery efficiency was studied through natural core and visual core displacement experiment. The results show that the oil–water interfacial tension is 9.1 × 10−4 mN/m when DPSB concentration is 0.5%, and it can still maintain low interfacial tension after adsorption. The interfacial tension can be further decreased by adding n-butyl alcoholy. When the DPSB concentration is 1.5% and n-butyl alcohol concentration is 1.5%, the microemulsion volume percentage is 31%, and the viscosity is 4.37 mPa∙s. It can reduce the flow velocity of displacing fluid, and expand sweep volume. Compared with water flooding, the residual oil saturation reduces by about 30% in microemulsion flooding. The recovery efficiency is increased by about 4% compared with the low concentration of surfactant flooding.

Bioretention system is one of the most used green stormwater infrastructures (GSI), and its media is a key factor in reducing runoff water volume and purifying water quality. Many studies have investigated media improvement to enhance the pollutant removal capacity. However, the long-term cumulative effect and microbial effect of pollutants in the modified-media bioretention system is less known. This study investigated the cumulative effect of pollutants and their influence on microbial characteristics in conventional and modified media bioretention system. The addition of modifiers increased the background content of pollutants in the media, and the accumulation of pollutants in planting soil (PS) and bioretention soil mixing + water treatment residuals (BSM+WTR) was relatively higher after the simulated rainfall experiment. The accumulation of pollutants led to a decrease in dehydrogenase activity, and an increase in urease and invertase activities. Ten dominant bacterial species at the phylum level were found in all bioretention systems. The relative abundances of the bacteria with good viability under low nutritional conditions decreased, while the species which could live in the pollutant-rich environment increased. The accumulation of pollutants in the bioretention system led to the extinction of some functional microorganisms. The better the effects of modified media on pollutant removal showed, the more obvious effect on the media micro-ecosystem was. To ensure the long-term efficient and stable operation of the modified-media bioretention system, we recommend balancing the pollutant removal efficiency and cumulative effect in modified-media bioretention systems.

Bisphenol A diglycidyl ether (BADGE)-based epoxy resin is one of the most widely used epoxy resins with an annual production amount of several million tons. Compared with all other legacy or emerging organic compounds, BADGE is special due to its toxicity and high reactivity in the environment. More and more studies are available on its analytical methods, occurrence, transformation and toxicity. Here, we provided a comprehensive review of the current BADGE-related studies, with focus on its production, application, available analytical methods, occurrences in the environment and human specimen, abiotic and biotic transformation, as well as the in vitro and in vivo toxicities. The available data show that BADGE and its derivatives are ubiquitous environmental chemicals and often well detected in human specimens. For their analysis, a water-free sample pretreatment should be considered to avoid hydrolysis. Additionally, their complex reactions with endogenous metabolites are areas of great interest. To date, the monitoring and further understanding of their transport and fate in the environment are still quite lacking, comparing with its analogues bisphenol A (BPA) and bisphenol S (BPS). In terms of toxicity, the summary of its current studies and Environmental Protection Agency (EPA) ToxCast toxicity database suggests BADGE might be an endocrine disruptor, though more detailed evidence is still needed to confirm this hypothesis in in vivo animal models. Future study of BADGE should focus on its metabolic transformation, reaction with protein and validation of its role as an endocrine disruptor. We believe that the elucidation of BADGEs can greatly enhance our understandings of those reactive compounds in the environment and human.

The octadentate hydroxypyridonate ligand 3,4,3-LI(1,2-HOPO) (t-HOPO) shows strong binding affinity with actinide cations and is considered as a promising decorporation agent used to eliminate in vivo actinides, while its dynamics in its unbound and bound states in the condensed phase remain unclear. In this work, by means of MD simulations, the folding dynamics of intact t-HOPO in its neutral (t-HOPO0) and in its deprotonated state (t-HOPO4−) were studied. The results indicated that the deprotonation of t-HOPO in the aqueous phase significantly narrowed the accessible conformational space under the simulated conditions, and it was prepared in a conformation that could conveniently clamp the cations. The simulation of UIV-t-HOPO showed that the tetravalent uranium ion was deca-coordinated with eight ligating O atoms from the t-HOPO4− ligand, and two from aqua ligands. The strong electrostatic interaction between the U4+ ion and t-HOPO4− further diminished the flexibility of t-HOPO4− and confined it in a limited conformational space. The strong interaction between the U4+ ion and t-HOPO4− was also implicated in the shortened residence time of water molecules.

Link prediction is an essential and challenging problem in research on complex networks, which can provide research tools and theoretical supports for the formation and evolutionary mechanisms of networks. Existing graph representation learning methods based on random walks usually ignore the influence of local network topology on the transition probability of walking nodes when predicting the existence of links, and the sampling strategy of walking nodes during random walks is uncontrolled, which leads to the inability of these methods to effectively learn high-quality node vectors to solve the link prediction problem. To address the above challenges, we propose a novel graph embedding method for link prediction. Specifically, we analyze the evolution mechanism of links based on triadic closure theory and use the network clustering coefficient to represent the aggregation ability of the network’s local structure, and this adaptive definition of the aggregation ability of the local structure enables control of the walking strategy of nodes in the random walking process. Finally, node embedding generated based on biased walking paths is employed to solve the link prediction problem. Extensive experiments and analyses show that the TCW algorithm provides high accuracy across a diverse set of datasets.

Octogen (HMX) is widely used as a high explosive and constituent in plastic explosives, nuclear devices, and rocket fuel. The direct discharge of wastewater generated during HMX production threatens the environment. In this study, we used the electrochemical oxidation (EO) method with a PbO2-based anode to treat HMX wastewater and investigated its degradation performance, mechanism, and toxicity evolution under different conditions. The results showed that HMX treated by EO could achieve a removal efficiency of 81.2% within 180 min at a current density of 70 mA/cm2, Na2SO4 concentration of 0.25 mol/L, interelectrode distance of 1.0 cm, and pH of 5.0. The degradation followed pseudo-first-order kinetics (R2 > 0.93). The degradation pathways of HMX in the EO system have been proposed, including cathode reduction and indirect oxidation by •OH radicals. The molecular toxicity level (expressed as the transcriptional effect level index) of HMX wastewater first increased to 1.81 and then decreased to a non-toxic level during the degradation process. Protein and oxidative stress were the dominant stress categories, possibly because of the intermediates that evolved during HMX degradation. This study provides new insights into the electrochemical degradation mechanisms and molecular-level toxicity evolution during HMX degradation. It also serves as initial evidence for the potential of the EO-enabled method as an alternative for explosive wastewater treatment with high removal performance, low cost, and low environmental impact.

Aims: Stratification of the risk of malignant arrhythmias in patients with coronary artery disease remains a challenge. This study evaluated the potential of high voltage J-waves in patients with acute ST-segment elevated myocardial infarction (STEMI) to predict the risk of malignant arrhythmias during hospitalization. Methods: A total of 128 consecutive STEMI patients with J-waves were enrolled within 48 h after the onset of the STEMI in this prospective study. The mean age was 62.97 ± 12.1 years, and 108 patients were male. Both 12-lead electrocardiograms (ECGs) and right-sided chest lead ECGs were recorded simultaneously within 10 min of admission to the hospital. Continuous ECG monitoring was administered from admission until discharge. Clinical characteristics and ECG parameters were compared between patients who survived and those who died during hospitalization. Results: Malignant ventricular arrhythmias (MVAs) were seen with J-waves more frequently in STEMI patients who subsequently died (P < 0.05). J-wave voltage, QTDc, Tp-e, and the Tp-e/QT ratio increased significantly in patients who died (P < 0.05). Multivariate logistic regression analysis revealed that J-wave voltage (odds ratio [OR], 89.09; 95% confidence interval [CI], 2.606-3045.108; P < 0.05) and MVAs (OR, 4.296; 95% CI, 1.348–13.693; P < 0.05) were associated with the occurrence of sudden death in patients with STEMI during hospitalization. Conclusions: High voltage J-waves are a potential ECG parameter for predicting sudden death in patients with STEMI during hospitalization.

Streams and rivers, especially in urban areas, are substantial sinks of bioavailable nitrogen owing to their hydrological connections with terrestrial systems and the intensity of inputs mediated by human activities. Denitrification can account for nitrogen removal in rivers, but its importance in urban rivers has been scarcely studied. This study chose 8 representative rivers in Shanghai, under different level of urbanization, and measured denitrification rates of sediments across a year. The results showed significant differences in temporal, spatial and vertical distributions (p

Complex networks with node attribute information are employed to represent complex relationships between objects. Research of attributed network embedding fuses the topology and the node attribute information of the attributed network in the common latent representation space, to encode the high-dimensional sparse network information to the low-dimensional dense vector representation, effectively improving the performance of the network analysis tasks. The current research on attributed network embedding is presently facing problems of high-dimensional sparsity of attribute eigenmatrix and underutilization of attribute information. In this paper, we propose a network embedding algorithm taking in a variational graph autoencoder (NEAT-VGA). This algorithm first pre-processes the attribute features, i.e., the attribute feature learning of the network nodes. Then, the feature learning matrix and the adjacency matrix of the network are fed into the variational graph autoencoder algorithm to obtain the Gaussian distribution of the potential vectors, which more easily generate high-quality node embedding representation vectors. Then, the embedding of the nodes obtained by sampling this Gaussian distribution is reconstructed with structural and attribute losses. The loss function is minimized by iterative training until the low-dimension vector representation, containing network structure information and attribute information of nodes, can be better obtained, and the performance of the algorithm is evaluated by link prediction experimental results.

Network representation learning is a machine learning method that maps network topology and node information into low-dimensional vector space. Network representation learning enables the reduction of temporal and spatial complexity in the downstream data mining of networks, such as node classification and graph clustering. Existing algorithms commonly ignore the global topological information of the network in network representation learning, leading to information loss. The complete subgraph in the network commonly has a community structure, or it is the component module of the community structure. We believe that the structure of the community serves as the revealed structure in the topology of the network and preserves global information. In this paper, we propose SF-NRL, a network representation learning algorithm based on complete subgraph folding. The algorithm preserves the global topological information of the original network completely, by finding complete subgraphs in the original network and folding them into the super nodes. We employ the network representation learning algorithm to study the node embeddings on the folded network, and then merge the embeddings of the folded network with those of the original network to obtain the final node embeddings. Experiments performed on four real-world networks prove the effectiveness of the SF-NRL algorithm. The proposed algorithm outperforms the baselines in evaluation metrics on community detection and multi-label classification tasks. The proposed algorithm can effectively generalize the global information of the network and provides excellent classification performance.

Space capture actuators face problems such as insufficient flexibility and electrical components that are vulnerable to extreme space environments. To address these problems, a centralized-driven flexible continuous robot based on a multiple scissor mechanism units is proposed in this study. The continuous robot body is composed of two scissor mechanism units coupled in series, and the base container’s three motors to drive the robot. The two scissor mechanism units ensure a wide range of flexible operations and the light weight of the robot. The centralized drive with three motors not only reduces the number of driving sources, but also ensures temperature control and protection of electrical components in the space environment. The kinematics and dynamics of the robot are analyzed, and the workspace and deformation performance of the robot are verified through experiments. Compared with other continuous robots, the proposed continuous robot retains the characteristics of continuous robots in a wide range of flexible operations. At the same time, the configuration is light and a small number of driving sources are used, which is suitable for extreme temperatures, vacuum, radiation, and strict resource-constrained environments in space.

Aiming at analyzing the temporal structures in evolutionary networks, we propose a community detection algorithm based on graph representation learning. The proposed algorithm employs a Laplacian matrix to obtain the node relationship information of the directly connected edges of the network structure at the previous time slice, the deep sparse autoencoder learns to represent the network structure under the current time slice, and the K-means clustering algorithm is used to partition the low-dimensional feature matrix of the network structure under the current time slice into communities. Experiments on three real datasets show that the proposed algorithm outperformed the baselines regarding effectiveness and feasibility.

The adsorption of uranyl on hydroxylated α-SiO2(001) in the presence of a series of anionic ligands, i.e. OHâ, CO32-, NO3-, H2PO4-, HPO42-, CH3COOâ (Acâ), C6H5COOâ (PhCO2-), C6H5Oâ (PhOâ), was studied by the periodic density functional theory (DFT) implemented in the Vienna ab initio simulation package (VASP). For the ligands other than OHâ and PhOâ, only the bidentate coordination modes to the uranyl were considered. The excess charge effect of a charged system was first evaluated by constructing models with net charge as is or neutralized by creating defect at the bottom of silica, and the results show that a neutralized model, even with defects, is more realistic than the charged ones. All uranyl species prefer to bind with the deprotonated site (î¸Oâ) rather than the protonated one (î¸OH), which suggests that the increase of pH, which leads to the deprotonation of the surface, may enhance the uranyl adsorption. On the other hand, the anionic ligands, which are formed at higher pH, have negative effects. The weaker acidic ligands, such as H2CO3, H3PO4 and H2O, whose speciation in solutions is sensitive to the fluctuation of pH, have more complex effect on the uranyl adsorption than strong acids or bases. Humic substances may coordinate with uranyl through carboxyl and phenolic groups, with the carboxyl group bound stronger. The ternary complexes with one bidentate (or monodentate) anion and one (or two) H2O as ligands, which leads to the uranyl penta-coordinated in its equatorial plane, are more favorable than other configurations when bound to the same anionic ligand. Both the charged nature and the coordination behavior of an anionic ligand are relevant to its ability to influence the adsorption of uranyl on the mineral surface. In addition, the uranyl species adsorbed at the surface functionalized by anionic ligands were also addressed, and the functionalized surfaces have weaker interaction with hydrated uranyl dication. Keywords: Density functional theory, Adsorption, α-SiO2(001), Uranyl, Anionic ligands

Based on the features of microemulsion flooding in low-permeability reservoir, a three-dimension three-phase five-component mathematical model for microemulsion flooding is established in which the diffusion and adsorption characteristics of surfactant molecules are considered. The non-Darcy flow equation is used to describe the microemulsion flooding seepage law in which the changes of threshold pressure gradient can be taken into account, and the correlation coefficients in the non-Darcy flow equation are determined through the laboratory experiments. A new treatment for the changes of threshold pressure and the quantitative description of adsorption quantity of surfactant and relative permeability curves are presented, which enhance the coincidence between mathematical model and experiment results. The relative errors of main development indexes are within 4%. A software is programmed based on the model to execute a core-level small-scale numerical simulation in Chaoyanggou Oilfield. The fitting relative errors of the pressure, flow rate, and moisture content are 3.25%, 2.71%, and 2.54%, respectively. The results of laboratory experiments and numerical simulation showed that microemulsion system could reduce the threshold pressure gradient by 0.010 MPa/m and injection pressure by 0.6 MPa. The biggest decline in moisture content reaches 33%, and the oil recovery is enhanced by 10.8%.

Methane (CH4) emissions from estuarine wetlands were proved to be influenced by tide movement and inundation conditions notably in many previous studies. Although there have been several researches focusing on the seasonal or annual CH4 emissions, the short-term CH4 emissions during the tide cycles were rarely studied up to now in this area. In order to investigate the CH4 emission pattern during a tide cycle in Yangtze Estuary salt marshes, frequent fixed-point observations of methane flux were carried out using the in-situ static closed chamber technique. The results indicated that the daily average CH4 fluxes varied from 0.68 mgCH4·m−2·h−1 to 4.22 mgCH4·m−2·h−1 with the average flux reaching 1.78 mgCH4·m−2·h−1 from small tide to spring tide in summer. CH4 fluxes did not show consistent variation with both tide levels and inundation time but increased steadily during almost the whole research period. By Pearson correlation analysis, CH4 fluxes were not correlated with both tide levels (R = −0.014, p = 0.979) and solar radiation (R = 0.024, p = 0.865), but significantly correlated with ambient temperature. It is temperature rather than the tide level mainly controlling CH4 emissions during the tide cycles. Besides, CH4 fluxes also showed no significant correlation with the underground pore-water CH4 concentrations, indicating that plant-mediated transport played a more important role in CH4 fluxes compared with its production and consumption.

Node embedding is a representation learning technique that maps network nodes into lower-dimensional vector space. Embedding nodes into vector space can benefit network analysis tasks, such as community detection, link prediction, and influential node identification, in both calculation and richer application scope. In this paper, we propose a two-step node embedding-based solution for the social influence maximization problem (IMP). The solution employs a revised network-embedding algorithm to map input nodes into vector space in the first step. In the second step, the solution clusters the vector space nodes into subgroups and chooses the subgroups’ centers to be the influential spreaders. The proposed approach is a simple but effective IMP solution because it takes both the social reinforcement and homophily characteristics of the social network into consideration in node embedding and seed spreaders selection operation separately. The information propagation simulation experiment of single-point contact susceptible-infected-recovered (SIR) and full-contact SIR models on six different types of real network data sets proved that the proposed social influence maximization (SIM) solution exhibits significant propagation capability.

Identifying vital nodes in complex networks is of paramount importance in understanding and controlling the spreading dynamics. Currently, this study is facing great challenges in dealing with big data in many real-life applications. With the deepening of the research, scholars began to realize that the analysis on traditional graph model is insufficient because many nodes in a multilayer network share connections among different layers. To address this problem both efficiently and effectively, a novel algorithm for identifying vital nodes in both monolayer and multilayer networks is proposed in this paper. Firstly, a node influence measure is employed to determine the initial leader of a local community. Subsequently, the community structures are revealed via the Maximum Influential Neighbors Expansion (MINE) strategy. Afterward, the communities are regarded as super-nodes for an iteratively folding process till convergence, in order to identify influencers hierarchically. Numerical experiments on 32 real-world datasets are conducted to verify the performance of the proposed algorithm, which shows superiority to the competitors. Furthermore, we apply the proposed algorithm in the graph of adjacencies derived from the maps of China and USA. The comparison and analysis of the identified provinces (or states) suggest that the proposed algorithm is feasible and reasonable on real-life applications.

The influence maximization problem (IMP) in complex networks is to address finding a set of key nodes that play vital roles in the information diffusion process, and when these nodes are employed as ”seed nodes”, the diffusion effect is maximized. First, this paper presents a refined network centrality measure, a refined shell (RS) index for node ranking, and then proposes an algorithm for identifying key node sets, namely the reject neighbors algorithm (RNA), which consists of two main sequential parts, i.e., node ranking and node selection. The RNA refuses to select multiple-order neighbors of the seed nodes, scatters the selected nodes from each other, and results in the maximum influence of the identified node set on the whole network. Experimental results on real-world network datasets show that the key node set identified by the RNA exhibits significant propagation capability.

Social network analysis is a multidisciplinary research covering informatics, mathematics, sociology, management, psychology, etc. In the last decade, the development of online social media has provided individuals with a fascinating platform of sharing knowledge and interests. The emergence of various social networks has greatly enriched our daily life, and simultaneously, it brings a challenging task to identify influencers among multiple social networks. The key problem lies in the various interactions among individuals and huge data scale. Aiming at solving the problem, this paper employs a general multilayer network model to represent the multiple social networks, and then proposes the node influence indicator merely based on the local neighboring information. Extensive experiments on 21 real-world datasets are conducted to verify the performance of the proposed method, which shows superiority to the competitors. It is of remarkable significance in revealing the evolutions in social networks and we hope this work will shed light for more and more forthcoming researchers to further explore the uncharted part of this promising field.

Automatic detection of arrhythmia is of great significance for early prevention and diagnosis of cardiovascular disease. Traditional feature engineering methods based on expert knowledge lack multidimensional and multi-view information abstraction and data representation ability, so the traditional research on pattern recognition of arrhythmia detection cannot achieve satisfactory results. Recently, with the increase of deep learning technology, automatic feature extraction of ECG data based on deep neural networks has been widely discussed. In order to utilize the complementary strength between different schemes, in this paper, we propose an arrhythmia detection method based on the multi-resolution representation (MRR) of ECG signals. This method utilizes four different up to date deep neural networks as four channel models for ECG vector representations learning. The deep learning based representations, together with hand-crafted features of ECG, forms the MRR, which is the input of the downstream classification strategy. The experimental results of big ECG dataset multi-label classification confirm that the F1 score of the proposed method is 0.9238, which is 1.31%, 0.62%, 1.18% and 0.6% higher than that of each channel model. From the perspective of architecture, this proposed method is highly scalable and can be employed as an example for arrhythmia recognition.

As a more complicated network model, multilayer networks provide a better perspective for describing the multiple interactions among social networks in real life. Different from conventional community detection algorithms, the algorithms for multilayer networks can identify the underlying structures that contain various intralayer and interlayer relationships, which is of significance and remains a challenge. In this paper, aiming at the instability of the label propagation algorithm (LPA), an improved label propagation algorithm based on the SH-index (SH-LPA) is proposed. By analyzing the characteristics and deficiencies of the H-index, the SH-index is presented as an index to evaluate the importance of nodes, and the stability of the SH-LPA algorithm is verified by a series of experiments. Afterward, considering the deficiency of the existing multilayer network aggregation model, we propose an improved multilayer network aggregation model that merges two networks into a weighted single-layer network. Finally, considering the influence of the SH-index and the weight of the edge of the weighted network, a community detection algorithm (MSH-LPA) suitable for multilayer networks is exhibited in terms of the SH-LPA algorithm, and the superiority of the mentioned algorithm is verified by experimental analysis.

In this experiment, terephthalyl alcohol was used as a modifier to modify phenol under both acidic and alkaline conditions to obtain modified phenols with different molecular structures. Subsequently, the modified phenols reacted with paraformaldehyde in an alkaline environment. After foaming and curing, a modified phenolic foam with high heat aging resistance was obtained. The molecular structure was characterized via Fourier transform infrared spectrometry (FT-IR) and nuclear magnetic resonance spectroscopy (13C NMR). The results showed that two different structures of phenolic resin can be successfully prepared under different conditions of acid and alkali. The modified phenolic foam was tested by thermogravimetric analysis. In addition, the modified phenolic foam was tested for mass change rate, dimensional change rate, powdering rate, water absorption rate, and compressive strength before and after aging. The results show that the modified phenolic foam has excellent performance. After heat aging for 24 h, the mass loss rate of the modified phenolic foam obtained by acid catalysis was as low as 4.5%, the pulverization rate was only increased by 3.2%, and the water absorption of the modified phenolic foam increased by 0.77%, which is one-third that of the phenolic foam. Compared with the phenolic foam, the modified phenolic foam shows good heat aging resistance.

Revealing the structural features of social networks is vitally important to both scientific research and practice, and the explosive growth of online social networks in recent years has brought us dramatic advances to understand social structures. Here we proposed a community detection approach based on user interaction behavior in weighted dynamic online social networks. We researched interaction behaviors in online social networks and built a directed and unweighted network model in terms of the Weibo following relationships between social individuals at the very beginning. In order to refine the interaction behavior, level one fuzzy comprehensive evaluation model was employed to describe how closely individuals are connected to each other. According to this intimate degree description, weights are tagged to the prior unweighted model we built. Secondly, a heuristic community detection algorithm for dynamic network was provided based on the improved version of modularity called module density. As for the heuristic rule, we chose greedy strategy and merely fed the algorithms with the changed parts within neighboring time slice. Experimental results show that the proposed algorithm can obtain high accuracy and simultaneously get comparatively lower time complexity than some typical algorithms. More importantly, our algorithm needs no a priori conditions.

Thorium (Th) is a natural radioactive element present in the environment and has the potential to be used as a nuclear fuel. Relatively little is known about the influence and toxicity of Th in the environment. In the present study, the toxicity of Th to the green algae Chlorella pyrenoidosa (C. pyrenoidosa) was evaluated by algal growth inhibition, biochemical assays and morphologic observations. In the cultural medium (OECD TG 201), Th(NO3)4 was transformed to amorphous precipitation of Th(OH)4 due to hydrolysis. Th was toxic to C. pyrenoidosa, with a 96 h half maximum effective concentration (EC50) of 10.4 μM. Scanning electron microscopy shows that Th-containing aggregates were attached onto the surface of the algal cells, and transmission electron microscopy indicates the internalization of nano-sized Th precipitates and ultrastructural alterations of the algal cells. The heteroagglomeration between Th(OH)4 precipitation and alga cells and enhanced oxidative stress might play important roles in the toxicity of Th. To our knowledge, this is the first report of the toxicity of Th to algae with its chemical species in the exposure medium. This finding provides useful information on understanding the fate and toxicity of Th in the aquatic environment.