iHPPPVis: Interactive Visual Analytics Approach for Production Performance Monitoring of Heavy-Plate Production Process.

Efficient monitoring of production performance is crucial for ensuring safe operations and enhancing the economic benefits of the Iron and Steel Corporation. Although basic modeling algorithms and visualization diagrams are available in many scientific platforms and industrial applications, there is still a lack of customized research in production performance monitoring. Therefore, this article proposes an interactive visual analytics approach for monitoring the heavy-plate production process (iHPPPVis). Specifically, a multicategory aggregated monitoring framework is proposed to facilitate production performance monitoring under varying working conditions. In addition, A set of visualizations and interactions are designed to enhance analysts' analysis, identification, and perception of the abnormal production performance in heavy-plate production data. Ultimately, the efficacy and practicality of iHPPPVis are demonstrated through multiple evaluations.

Exploration on pharmacological mechanisms of YZP against neuropathic pain via inhibiting spinal inflammation and the rationality of its compatibility.

ETHNOPHARMACOLOGICAL RELEVANCE: Yuanhu Zhitong Prescription (YZP) is a well-known traditional Chinese medicine (TCM) formula for neuropathic pain (NP) therapy with a satisfying clinical efficacy. However, the underlying pharmacological mechanism and its compatibility principle remain unclear. AIM OF THE STUDY: This study aims to investigate the analgesic and compatibility mechanisms of YZP on neuropathic pain (NP) at the gene and biological process levels. MATERIALS AND METHODS: The chronic constriction injury (CCI) rats were intragastrically administrated with extracts of YZP, YH and BZ separately, and then mechanical hypersensitivity were measured to evaluate the analgesic effects between YH and BZ before and after compatibility. Then, RNA-seq and bioinformatics analyses were performed to elucidate the potential mechanisms underlying YZP's analgesia and compatibility. Finally, the expression levels and significant differences of key genes were analyzed. RESULTS: Behaviorally, both YZP and YH effectively alleviated mechanical allodynia in CCI rats, with YZP being superior to YH. In contrast, we did not observe an analgesic effect of BZ. Genetically, YZP, YH, and BZ reversed the expression levels of 52, 34, and 42 aberrant genes in the spinal cord of CCI rats, respectively. Mechanically, YZP was revealed to alleviate NP mainly by modulating the inflammatory response and neuropeptide signaling pathway, which are the dominant effective processes of YH. Interestingly, the effective targets of YZP were especially enriched in leukocyte activation and cytokine-mediated signaling pathways. Moreover, BZ was found to exert an adjunctive effect in enhancing the analgesic effect of YH by promoting skeletal muscle tissue regeneration and modulating calcium ion transport. CONCLUSIONS: YH, as the monarch drug, plays a dominant role in the analgesic effect of YZP that effectively relieves NP by inhibiting the spinal inflammation and neuropeptide signaling pathway. BZ, as the minister drug, not only synergistically enhances analgesic processes of YH but also helps to alleviate the accompanying symptoms of NP. Consequently, YZP exerted a more potent analgesic effect than YH and BZ alone. In conclusion, our findings offer new insights into understanding the pharmacological mechanism and compatibility principle of YZP, which may support its clinical application in NP therapy.

Intelligent condition monitoring with CNN and signal enhancement for undersampled signals.

High-frequency signals like vibration and acoustic emission are crucial for condition monitoring, but their high sampling rates challenge data acquisition, especially for online monitoring. Our research developed a novel method for condition identification in undersampled signals using a modified convolutional neural network integrated with a signal enhancement approach. A frequency-domain filtering is applied to suppress similar sidebands and obtain more discriminative features of different conditions, followed by an interpolation-based upsampling in the time domain to restore the signal length and strengthen the low-frequency harmonic information. Enhanced signals are converted into two-dimensional grayscale images for neural network analysis. Tested on bearing datasets and real-world data from regenerative thermal oxidizer lift valve leakage, our method effectively extracts features from low-frequency signals, achieving over 95% fault identification accuracy.

A retrospective study on iRoot BP Plus full pulpotomy for primary molars with partial irreversible pulpitis.

OBJECTIVES: This study aimed to observe the outcomes of iRoot BP Plus full pulpotomy in primary molars with partial irreversible pulpitis retrospectively. METHODS: Collect 102 cases of primary molars with partial irreversible pulpitis undergoing iRoot BP Plus full pulpotomy from January 2019 to August 2023, with a follow-up period of 24-47 months. Based on the presence of irreversible pulpitis symptoms before surgery, the included cases will be divided into asymptomatic group (n=53) and symptomatic group (n=49). Observe the clinical and imaging success rates of both groups. RESULTS: Clinical success rates were 96.2% and 97.9% in asymptomatic and symptomatic groups, and radiographic success rates were 96.2% and 93.9% respectively. CONCLUSIONS: iRoot BP Plus full pulpotomy can be used for the treatment of primary molars with partial irreversible pulpitis under an enhanced pulpotomy protocol.

Fuzzy State-Driven Cross-Time Spatial Dependence Learning for Multivariate Time-Series Anomaly Detection.

Cross-time spatial dependence (i.e., the interaction between different variables at different time points) is indispensable for detecting anomalies in multivariate time series, as certain anomalies may have time delays in their propagation from one variable to another. However, accurately capturing cross-time spatial dependence remains a challenge. Specifically, real-world time series usually exhibits complex and incomprehensible evolutions that may be compounded by multiple temporal states (i.e., temporal patterns, such as rising, fluctuating, and peak). These temporal states mix and overlap with each other and exhibit dynamic and heterogeneous evolution laws in different time series, making the cross-time spatial dependence extremely intricate and mutable. Therefore, a cross-time spatial graph network with fuzzy embedding is proposed to disentangle latent and mixing temporal states and exploit it to meticulously learn cross-time spatial dependence. First, considering that temporal states are diversiform and their mixing modes are unknown, we introduce a fuzzy state set to uniformly characterize potential temporal states and adaptively generate corresponding membership degrees to depict how these states mix. Further, we propose a cross-time spatial graph, quantifying similarities among fuzzy states and sensing their dynamic evolutions, to flexibly learn mutable cross-time spatial dependence. Finally, we design state diversity and temporal proximity constraints to ensure the differences among fuzzy states and the evolution continuity of fuzzy states. Experiments on real-world datasets show that the proposed model outperforms the state-of-the-art models.

Analysis of intestinal flora and cognitive function in maintenance hemodialysis patients using combined 16S ribosome DNA and shotgun metagenome sequencing.

BACKGROUND: Cognitive impairment is widely prevalent in maintenance hemodialysis (MHD) patients, and seriously affects their quality of life. The intestinal flora likely regulates cognitive function, but studies on cognitive impairment and intestinal flora in MHD patients are lacking. METHODS: MHD patients (36) and healthy volunteers (18) were evaluated using the Montreal Cognitive Function Scale, basic clinical data, and 16S ribosome DNA (rDNA) sequencing. Twenty MHD patients and ten healthy volunteers were randomly selected for shotgun metagenomic analysis to explore potential metabolic pathways of intestinal flora. Both16S rDNA sequencing and shotgun metagenomic sequencing were conducted on fecal samples. RESULTS: Roseburia were significantly reduced in the MHD group based on both 16S rDNA and shotgun metagenomic sequencing analyses. Faecalibacterium, Megamonas, Bifidobacterium, Parabacteroides, Collinsella, Tyzzerella, and Phascolarctobacterium were positively correlated with cognitive function or cognitive domains. Enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways included oxidative phosphorylation, photosynthesis, retrograde endocannabinoid signaling, flagellar assembly, and riboflavin metabolism. CONCLUSION: Among the microbiota, Roseburia may be important in MHD patients. We demonstrated a correlation between bacterial genera and cognitive function, and propose possible mechanisms.

Gastroesophageal reflux disease increases the risk of essential hypertension: results from the Nationwide Readmission Database and Mendelian randomization analysis.

BACKGROUND: The link between gastroesophageal reflux disease (GERD) and essential hypertension (EH) and its causal nature remains controversial. Our study examined the connection between GERD and the risk of hypertension and assessed further whether this correlation has a causal relationship. METHODS: First, we utilized the National Readmission Database including 14 422 183 participants to conduct an observational study. Dividing the population into GERD and non-GERD groups, we investigated the correlation between GERD and EH using multivariate logistic regression. Next, bidirectional two-sample Mendelian randomization was adopted. The summary statistics for GERD were obtained from a published genome-wide association study including 78 707 cases and 288 734 controls. We collected summary statistics for hypertension containing 70 651 cases and 223 663 controls from the FinnGen consortium. We assessed causality primarily by the inverse-variance weighted method with validation by four other Mendelian randomization approaches as well as an array of sensitivity analyses. RESULTS: In the unadjusted model, GERD patients had a higher risk of EH than the non-GERD group, regardless of gender (odds ratio, 1.43; 95% confidence interval: 1.42-1.43; P < .001). Further adjusting for critical confounders did not change this association. For Mendelian randomization, we found that genetically predicted GERD was causally linked to an enhanced risk of EH in inverse-variance weighted technique (odds ratio, 1.52; 95% confidence interval: 1.39-1.67; P = 3.51 × 10-18); conversely, EH did not raise the risk of GERD causally. CONCLUSIONS: GERD is a causal risk factor for EH. Further research is required to probe the mechanism underlying this causal connection.

Variational Bayesian Student's-t Mixture Model With Closed-Form Missing Value Imputation for Robust Process Monitoring of Low-Quality Data.

Due to record errors, transmission interruptions, etc., low-quality process data, including outliers and missing data, commonly exist in real industrial processes, challenging the accurate modeling and reliable monitoring of the operating statuses. In this study, a novel variational Bayesian Student's-t mixture model (VBSMM) with a closed-form missing value imputation method is proposed to develop a robust process monitoring scheme for low-quality data. First, a new paradigm for the variational inference of Student's-t mixture model is proposed to develop a robust VBSMM model, which optimizes the variational posteriors in an extended feasible region. Second, conditioned on the complete and partially missing data information, a closed-form missing value imputation method is derived to address the challenges of outliers and multimodality in accurate data recovery. Then, a robust online monitoring scheme that can maintain its fault detection performance in the presence of poor data quality is developed, where a novel monitoring statistic called the expected variational distance (EVD) is first proposed to quantify the changes in operating conditions and can be easily extended to other variational mixture models. Case studies on a numerical simulation and a real-world three-phase flow facility illustrate the superiority of the proposed method in missing value imputation and fault detection of low-quality data.

Explicit Representation and Customized Fault Isolation Framework for Learning Temporal and Spatial Dependencies in Industrial Processes.

Typically, industrial processes possess both temporal and spatial dependencies due to intravariable dynamics and intervariable couplings. The two dependencies have different manifestations, indicating diverse process characteristics. However, the existing methods fail to separate temporal and spatial information well, leading to inappropriate representation and inaccurate fault detection and isolation results. This study proposes an explicit representation and customized fault isolation framework to tackle temporal and spatial characteristics, so as to identify and locate anomalies affecting different dependencies. First, we design a double-level separation method for temporal and spatial information. In the first level, we construct two independent auto-encoding modules to extract temporal correlation and spatial graph structure in parallel. In the second level, we propose an information aliasing loss function to guild the two modules to distinguish between temporal and spatial characteristics, further facilitating information separation. By monitoring the explicit temporal and spatial statistics obtained by the two modules, spatiotemporal dependencies of anomalies can be determined for subsequent isolation. Furthermore, we propose a customized isolation strategy for anomalies in temporal and spatial characteristics. By quantifying changes in intravariable temporal dynamics and intervariable spatial graph structure individually, temporal impact and spatial propagation of faults can be finely characterized and isolated. Three examples are adopted to verify the performance of the proposed framework, including a numerical example, a real condensing system of the thermal power plant process, and the Tennessee Eastman benchmark process.

Genetically predicted gut microbiome and risk of oral cancer.

PURPOSE: Mounting evidence suggests a possible link between gut microbiome and oral cancer, pointing to some potential modifiable targets for disease prevention. In the present study, Mendelian randomization (MR) was used to explore whether there was a causal link between gut microbiome and oral cancer. METHODS: The single nucleotide polymorphisms (SNPs) significantly associated with gut microbiome were served as instrumental variables. MR analyses were performed using genetic approaches such as inverse variance weighting (IVW), MR Egger and weighted median, with IVW as the primary approach, supplemented by MR Egger and weighted median. Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) and MR-Egger regression were used to detect the presence of horizontal pleiotropy and identify outlier SNPs. RESULTS: Causal effect estimates indicated that genetically predicted abundance of Prevotellaceae was associated with higher risk of oral cancer (odds ratio (OR) 1.80, 95% confidence interval (CI) 1.16-2.81, p = 0.009). There was no evidence of notable heterogeneity and horizontal pleiotropy. CONCLUSION: Genetically derived estimates suggest that Prevotellaceae may be associated with the risk of oral cancer. Such robust evidence should be given priority in future studies and explore the underlying mechanisms.

Recent advances in aggregation-induced emission-active type I photosensitizers with near-infrared fluorescence: From materials design to therapeutic platform fabrication.

Near-infrared (NIR) fluorescence imaging-guided photodynamic therapy (PDT) technology plays an important role in treating various diseases and still attracts increasing research interests for developing novel photosensitizers (PSs) with outstanding performances. Conventional PSs such as porphyrin and rhodamine derivatives have easy self-aggregation properties in the physiological environment due to their inherent hydrophobic nature caused by their rigid molecular structure that induces strong intermolecular stacking π-π interaction, leading to serious fluorescence quenching and cytotoxic reactive oxygen species (ROS) reduction. Meanwhile, hypoxia is an inherent barrier in the microenvironment of solid tumors, seriously restricting the therapeutic outcome of conventional PDT. Aforementioned disadvantages should be overcome urgently to enhance the therapeutic effect of PSs. Novel NIR fluorescence-guided type I PSs with aggregation-induced emission (AIE), which features the advantages of improving fluorescent intensity and ROS generation efficiency at aggregation as well as outstanding oxygen tolerance, bring hope for resolving aforementioned problems simultaneously. At present, plenty of research works fully demonstrates the advancement of AIE-active PDT based on type I PSs. In this review, cutting-edge advances focusing on AIE-active NIR type I PSs that include the aspects of the photochemical mechanism of type I ROS generation, various molecular structures of reported type I PSs with NIR fluorescence and their design strategies, and typical anticancer applications are summarized. Finally, a brief conclusion is obtained, and the underlying challenges and prospects of AIE-active type I PSs are proposed.

Inhibitory effect of bilobalide on Staphylococcus aureus von Willebrand factor-binding protein and its therapeutic effect in mice with pneumonia.

AIMS: Disabling bacterial virulence with small molecules has been proposed as a potential strategy to prevent bacterial pathogenicity. The von Willebrand factor-binding protein of Staphylococcus aureus was identified previously as a key virulence determinant. Our objective was to discover a von Willebrand-factor binding protein (vWbp) inhibitor distinct from the antibiotics used to prevent infections resulting from S. aureus. METHODS AND RESULTS: Using coagulation assays, we found that the sesquiterpene trilactone bilobalide blocks coagulation mediated by vWbp, but has no impact on the growth of S. aureus at a concentration of 128 µg ml-1. Moreover, a mouse model of pneumonia caused by S. aureus indicated that bilobalide could attenuate S. aureus virulence in vivo. This effect is achieved not by interfering with the expression of vWbp but by binding to vWbp, as demonstrated by western blotting, thermal shift assays, and fluorescence quenching assays. Using molecular dynamic simulations and point mutagenesis analysis, we identified that the Q17A and R453A residues are key residues for the binding of bilobalide to vWbp. CONCLUSIONS: Overall, we tested the ability of bilobalide to inhibit S. aureus infections by targeting vWbp and explored the potential mechanism of this activity.

Echinacoside, a promising sortase A inhibitor, combined with vancomycin against murine models of MRSA-induced pneumonia.

Methicillin-resistant Staphylococcus aureus (MRSA) is a pathogenic bacterium responsible for a range of severe infections, such as skin infections, bacteremia, and pneumonia. Due to its antibiotic-resistant nature, current research focuses on targeting its virulence factors. Sortase A (SrtA) is a transpeptidase that anchors surface proteins to the bacterial cell wall and is involved in adhesion and invasion to host cells. Through fluorescence resonance energy transfer (FRET), we identified echinacoside (ECH), a natural polyphenol, as a potential SrtA inhibitor with an IC50 of 38.42 µM in vitro. It was demonstrated that ECH inhibited SrtA-mediated S. aureus fibrinogen binding, surface protein A anchoring, and biofilm formation. The fluorescence quenching assay determined the binding mode of ECH to SrtA and calculated the KA-binding constant of 3.09 × 105 L/mol, demonstrating the direct interaction between the two molecules. Molecular dynamics simulations revealed that ECH-SrtA interactions occurred primarily at the binding sites of A92G, A104G, V168A, G192A, and R197A. Importantly, the combination of ECH and vancomycin offered protection against murine models of MRSA-induced pneumonia. Therefore, ECH may serve as a potential antivirulence agent against S. aureus infections, either alone or in combination with vancomycin.

Inhibitory Effect of Salicin on Staphylococcus aureus Coagulase.

The massive use of antibiotics has resulted in an alarming increase in antibiotic resistance in Staphylococcus aureus (S. aureus). This study aimed to identify the inhibitory effect of salicin on S. aureus. Coagulase (Coa) activity was assessed using in vitro Coa assays and Western blot, thermal shift assay (TSA), fluorescence quenching and molecular docking experiments were conducted to verify the interaction between salicin and Coa. An in vivo mouse pneumonia model demonstrated that salicin can reduce the virulence of S. aureus. In vitro Coa assays elucidated that salicin directly inhibited Coa activity. The Western blot and TSA results suggested that salicin did not block the expression of Coa but affected the thermal stability of the protein by binding to Coa. The fluorescence quenching, molecular docking and molecular dynamics assays have found that the most promising binding site between salicin and Coa was GLN-97. The pneumonia model of mice infected with S. aureus revealed that salicin could not only reduce the content of lung bacteria in mice but also prolong their survival. Salicin was identified as a novel anti-infective candidate compound with the potential to target Coa and inhibit its activity by binding to it, which would facilitate the development of roadmaps for future research.

Levo-tetrahydropalmatine ameliorates neuropathic pain by inhibiting the activation of the Clec7a-MAPK/NF-κB-NLRP3 inflammasome axis.

BACKGROUND: Because of the complex pathogenesis of neuropathic pain (NP), the therapeutic efficacy of existing drugs is not satisfactory. Accumulating studies have indicated that neuroinflammation may play a key role in NP onset and progression. Levo-tetrahydropalmatine (l-THP) has been extensively used for relieving chronic pain for decades. However, its potential mechanisms against NP have not yet been fully elucidated. PURPOSE: Exploring and elucidating the therapeutic effect and pharmacological mechanism of l-THP in treating NP. METHODS: RNA-seq and bioinformatics analyses were carried out to identify effective target profiling of I-THP in chronic constrictive injury (CCI) rats. The I-THP related hub targets and signaling pathways were obtained via bioinformatics analysis, then subjected to in-depth analyses through experiments in vivo. A gain-of-function study further confirmed the role of Clec7a in l-THP-mediated pain relief. Finally, the interaction between l-THP and Clec7a was verified through molecular docking and surface plasmon resonance (SPR). RESULTS: l-THP treatment effectively alleviated mechanical and thermal allodynia in NP model rats. Functionally, the I-THP effective targets were mainly enriched in inflammatory response-related pathways. Furthermore, Clec7a-MAPK/NF-κB-NLRP3 inflammasome axis was selected as one of the potential pathways of l-THP against NP. Mechanically, l-THP markedly reduced CCI-induced Clec7a overexpression, significantly inhibited the Clec7a-triggered phosphorylation of MAPK and NF-κB-p65, and decreased the expression of pyroptosis-related protein NLRP3 and Caspase-1-p20. The analgesic effect of l-THP on NP was partly eliminated when transfecting the overexpression vector virus pLVSO5Clec7a. Importantly, molecular docking and SPR data revealed that l-THP directly binds with the Clec7a protein. CONCLUSION: This study is the first to indicate that l-THP may exert an analgesic effect through inhibiting neuroinflammation via the Clec7a-MAPK/NF-κB-NLRP3 inflammasome axis, supporting the clinical utility of l-THP in NP therapy.

The effect of air pollution exposure on the risk of outpatient visits for periodontitis: a time-series study.

This study aimed to determine if air pollution affected the risk of periodontitis outpatient visits. We collected the records of 56,456 periodontitis outpatient visits in Hefei, China, from January 1，2014 to December 31，2021. The relationship between air pollution and periodontitis outpatient visits was evaluated using distributed lag nonlinear and generalized linear models. Additional analyses were performed, stratifying the data by age, season, and sex. Subgroup analyses showed a significantly higher risk of periodontitis outpatient visits due to NO2 exposure during the warm season compared with the cold season. Moreover, O3 exposure was associated with a lower risk of periodontitis outpatient visits in the cold season. The findings suggest that NO2 exposure is associated with an increased risk of periodontitis outpatient visits, whereas O3 exposure is associated with a decreased risk of periodontitis outpatient visits. Season is found to be an effect modifier in these associations.

Is Etiology a Key Factor for Regenerative Endodontic Treatment Outcomes?

INTRODUCTION: This study aimed to evaluate treatment outcomes of regenerative endodontic treatment (RET) in nonvital immature permanent teeth due to developmental malformation and trauma, and to analyze the influence of etiology on the prognosis. METHODS: Fifty-five cases were included and divided into a malformation group (n = 33) and a trauma group (n = 22). Treatment outcomes were classified as healed, healing, and failure. Root development was evaluated in terms of root morphology and the percentage changes in root length, root width, and apical diameter during a follow-up period of 12-85 months (mean 30.8 months). RESULTS: The mean age and the mean degree of root development in the trauma group were significantly younger than that in the malformation group. The success rate of RET was 93.9% (81.8% healed, 12.1% healing) in the malformation group and 90.9% (68.2% healed, 22.7% healing) in the trauma group, showing no statistically significant difference. The proportion of type I-III root morphology in the malformation group (97%, 32/33) was significantly higher than that in the trauma group (77.3%, 17/22) (P < .05), whereas there was no significant difference in the percentage changes of root length, root width, and apical diameter between the 2 groups. Six cases (6/55, 10.9%) showed no significant root development (type IV-V) (1 in the malformation group and 5 in the trauma group). Six cases (6/55, 10.9%) revealed intracanal calcification. CONCLUSIONS: RET achieved reliable outcomes regarding the healing of apical periodontitis and continued root development. The etiology seems to influence the outcome of RET. Malformation cases presented with a better prognosis than trauma cases after RET.

Cu/Ag-Mediated One-Pot Enantioselective Synthesis of Fully Decorated 1,2,3-Triazolo[1,5-a]pyrazines.

The synthesis of chiral triazole-fused pyrazine scaffolds from readily available substrates in a step-economical asymmetric catalytic way is highly appealing. We herein report that an efficient Cu/Ag relay catalyzed protocol employing cascade asymmetric propargylic amination, hydroazidation, and [3 + 2] cycloaddition reaction with high efficiency to access the target enantioenriched 1,2,3-triazolo[1,5-a]pyrazine has been accomplished by applying a novel N,N,P-ligand. The one-pot reaction of three components exhibits high functional group tolerance, excellent enantioselectivities, and a broad substrate scope with readily available starting materials.

Effects of different types of granular activated carbon on methanogenesis of carbohydrate-rich food waste: Performance, microbial communities and optimization.

Granular activated carbon (GAC) supplementation is an efficient method for enhancing methane production during the anaerobic digestion of food waste, but it remains unclear which type of GAC is optimal and what potential mechanisms are involved with different types of GAC, particularly for the methanogenic system of carbohydrate-rich food waste. This study selected three commercial GAC (GAC#1, GAC#2, GAC#3) with very distinct physical and chemical properties, and investigated their impacts on the methanogenesis of carbohydrate-rich food waste with an inoculation/substrate ratio of 1. Results indicated that Fe-doped GAC#3 had a lower specific surface area but higher conductivity, yet exhibited superior performance in facilitating methanogenesis compared with GAC#1 and GAC#2, which possessed larger specific surface areas. The addition of 10 g/L GAC#3 enhanced the methane yield by 10-folds through regulating pH levels, alleviating volatile fatty acids-induced stress, enhancing key enzymatic activity, as well as enriching direct interspecies electron transfer-mediated syntrophic partner of Syntrophomonas with Methanosarcina. Furthermore, GAC#1, which had the largest specific surface area but exhibited the poorest performance, was chemically modified to enhance its ability in promoting methanogenesis. The resulting material, named MGAC#1 (Fe3O4-loaded GAC#1), exhibited superior electro-conductivity and high methane production efficiency. The methane yield of 588 mL/g-VS showed a remarkable increase of 468 % compared with GAC#1, and a modest increase of 13 % compared with GAC#3, surpassing most values reported in literature. These findings suggested that the Fe3O4-loaded GAC with lager specific surface area, was the optimal choice for the methanogenesis of sole readily acidogenic waste, providing valuable insights for the preparation of superior-quality GAC for application in biogas industry.

Hyperspectral Image Classification With Multi-Attention Transformer and Adaptive Superpixel Segmentation-Based Active Learning.

Deep learning (DL) based methods represented by convolutional neural networks (CNNs) are widely used in hyperspectral image classification (HSIC). Some of these methods have strong ability to extract local information, but the extraction of long-range features is slightly inefficient, while others are just the opposite. For example, limited by the receptive fields, CNN is difficult to capture the contextual spectral-spatial features from a long-range spectral-spatial relationship. Besides, the success of DL-based methods is greatly attributed to numerous labeled samples, whose acquisition are time-consuming and cost-consuming. To resolve these problems, a hyperspectral classification framework based on multi-attention Transformer (MAT) and adaptive superpixel segmentation-based active learning (MAT-ASSAL) is proposed, which successfully achieves excellent classification performance, especially under the condition of small-size samples. Firstly, a multi-attention Transformer network is built for HSIC. Specifically, the self-attention module of Transformer is applied to model long-range contextual dependency between spectral-spatial embedding. Moreover, in order to capture local features, an outlook-attention module which can efficiently encode fine-level features and contexts into tokens is utilized to improve the correlation between the center spectral-spatial embedding and its surroundings. Secondly, aiming to train a excellent MAT model through limited labeled samples, a novel active learning (AL) based on superpixel segmentation is proposed to select important samples for MAT. Finally, to better integrate local spatial similarity into active learning, an adaptive superpixel (SP) segmentation algorithm, which can save SPs in uninformative regions and preserve edge details in complex regions, is employed to generate better local spatial constraints for AL. Quantitative and qualitative results indicate that the MAT-ASSAL outperforms seven state-of-the-art methods on three HSI datasets.