The long granulation time and instability of aerobic granular sludge (AGS) have restricted its large-scale application significantly. The objective of this review is to discuss the recent ...achievements and progress made in the field of rapid granulation and granule stability. To this end, we review more than one hundred studies in this field. In this review, four fast granulation methods, namely the addition of metal ions, the addition of a carrier, seeding with AGS, and inoculation using special strains are considered. Three stability enhancement strategies, namely, the improvement of the influent substrate, the selection of slow-growing microorganisms, and the addition of carrier particles, are also discussed. Finally, the potential of these strategies to accelerate granulation and increase the stability of granules in large-scale applications is discussed. In addition, combining a carrier with other strategies to promote granulation and improve stability is proposed. Furthermore, the prospect of enhancing the transmission of signal molecules in quorum sensing systems to promote sludge granulation and improve granule stability are discussed.
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•Formation and stability enhancement of aerobic granular sludge (AGS) are reviewed.•Effects of carrier and QS on fast granulation and stability of AGS are discussed.•New methods for rapid granulation and stability enhancement of AGS are proposed.
A comprehensive review on achievements and progress in rapid granulation and the maintenance of granule stability of the AGS technology.
The prevention/treatment of osteomyelitis infection by combining the local antibiotic delivery system with the bone regeneration scaffold can effectively overcome the drawbacks of systemic antibiotic ...administration and realize the full cycle control of drug release. Herein, by introducing sodium carboxymethyl cellulose (SCMC) cross-linking agent to improve the binding force of poly(lactic-co-glycolic acid) (PLGA) microspheres and porous hydroxyapatite (HAp) bone scaffold. The porous HAp/PLGA drug-loaded microsphere bone scaffold for gentamicin sulfate (GS) delivery was successfully prepared. The optimal preparation parameters, drug release characteristics, SCMC enhancement mechanism, antibacterial properties and bone cell activity of porous HAp/PLGA drug-loaded microsphere bone scaffolds were comprehensively investigated. The results showed that the 0.1% SCMC-modified porous HAp/PLGA drug-loaded microsphere bone scaffold has a cumulative drug release of 45.0 ± 0.90% on the first day, which is about 20% lower than that of pure PLGA drug-loaded microspheres. Moreover, its drug release can be sustained and stably released for more than 17 d, which is attributed to the enhancement of the binding force between the microspheres and HAp by SCMC (combination for more than 3 weeks). Meanwhile, the diameter of the antibacterial ring expanded from the initial 10 ± 0.50 to 28 ± 1.2 mm after 14 d, which also indicated the sustained and stable release of GS. Alamar Blue analysis results showed that 0.1% SCMC-modified composite bone scaffold is beneficial to the proliferation activity of bone cells, and its 14 day activity increased by 20%. The above results indicate that the SCMC-modified composite bone scaffold has the potential to treat/prevent osteomyelitis.
Summary
Biomass is one of the most promising clean energy sources. The porous carbon materials prepared by biomass as electrode materials of electric double‐layer capacitors (EDLCs) are easily ...available at a low price, which would greatly reduce the cost of the production. However, carbon materials made with biomass generally have many disadvantages such as low specific surface area (SSA), poor pore size structure, and difficulty to control the pore diameter, which results in the poor EDLC performance. In this paper, the prime purpose is to expose the recent progress of biomass carbon in the fields of electrode materials of EDLC. The review provides a comprehensive literature review that is focused on EDLC electrodes derived from biochar of the evidence of 181 publications published over a period of 30 years from 1989 to 2019. Various carbon materials derived from different biomass for electrode of EDLC are discussed. The most promising methods for the preparation of several biomass carbons are described in detail. Some factors such as SSA, pore size structure, surface functional groups, and electrolyte are further analyzed to discuss the effects on the electrochemical performance of the EDLC. Notably, current deficiencies and possible solutions of preparation methods of biomass carbon as electrode materials are outlined. And the future research trends in this field are prospected.
The review summarizes the progress and defect of biochar as electrode for EDLC over a period of 30 years from 1989 to 2019. Various carbon materials derived from different biomass for electrode of EDLC are discussed. The most promising methods for the preparation of several biomass carbons are described in detail. Some factors such as SSA, pore size structure, surface functional groups, and electrolyte are further analyzed to discuss the effects on the electrochemical performance of the EDLC.
Winter wheat is one of the most important crops in the world. It is great significance to obtain the planting area of winter wheat timely and accurately for formulating agricultural policies. Due to ...the limited resolution of single SAR data and the susceptibility of single optical data to weather conditions, it is difficult to accurately obtain the planting area of winter wheat using only SAR or optical data. To solve the problem of low accuracy of winter wheat extraction only using optical or SAR images, a decision tree classification method combining time series SAR backscattering feature and NDVI (Normalized Difference Vegetation Index) was constructed in this paper. By synergy using of SAR and optical data can compensate for their respective shortcomings. First, winter wheat was distinguished from other vegetation by NDVI at the maturity stage, and then it was extracted by SAR backscattering feature. This approach facilitates the semi-automated extraction of winter wheat. Taking Yucheng City of Shandong Province as study area, 9 Sentinel-1 images and one Sentinel-2 image were taken as the data sources, and the spatial distribution of winter wheat in 2022 was obtained. The results indicate that the overall accuracy (OA) and kappa coefficient (Kappa) of the proposed method are 96.10% and 0.94, respectively. Compared with the supervised classification of multi-temporal composite pseudocolor image and single Sentinel-2 image using Support Vector Machine (SVM) classifier, the OA are improved by 10.69% and 5.66%, respectively. Compared with using only SAR feature for decision tree classification, the producer accuracy (PA) and user accuracy (UA) for extracting the winter wheat are improved by 3.08% and 8.25%, respectively. The method proposed in this paper is rapid and accurate, and provide a new technical method for extracting winter wheat.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
This study aimed to compare the efficacy of different antibiotic classes and dosages in preventing maternal infection after cesarean delivery.
Databases were searched for randomized controlled trials ...(RCTs) published between January 1980 and January 2021 on antibiotic use for the prevention of maternal infection after cesarean delivery. The outcomes were endometritis, febrile morbidity, and wound infection, reported as odds ratios (OR) and surface under the cumulative ranking curve analysis scores.
A total of 31 RCTs met the inclusion criteria. In the network meta-analysis (NMA) for endometritis, pooled network OR values indicated that the following interventions were superior to placebo: cephalosporins (OR: 0.18, 95% credibility interval CrI: 0.07-0.45), penicillins (OR: 0.19, 95% CrI: 0.07-0.50), penicillins (multiple doses) (OR: 0.20, 95% CrI: 0.05-0.65), combination therapies (OR: 0.22, 95% CrI: 0.09-0.54), and cephalosporins (multiple doses) (OR: 0.25, 95% CrI: 0.08-0.74). In the NMA for febrile morbidity, placebo was more effective than the other interventions. In the NMA for wound infection, pooled network OR values indicated that the following interventions were superior to placebo: penicillin (OR: 0.14, 95% CrI: 0.05-0.37), cephalosporins (OR: 0.19, 95% CrI: 0.08-0.41), cephalosporins (multiple doses) (OR: 0.20, 95% CrI: 0.06-0.58), combination therapies (OR: 0.29, 95% CrI: 0.13-0.57), macrolides (OR: 0.33, 95% CrI: 0.15-0.74), and penicillins (multiple doses) (OR: 0.40, 95% CrI: 0.17-0.91).
Compared with placebo, a single dose of commonly used antibiotics may prevent maternal infection after cesarean delivery. However, the incidence of febrile morbidity was not reduced.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Temporal knowledge graph (TKG) reasoning, as an essential research direction in natural language processing, focuses on capturing the dynamic changes in entities and relationships over time. However, ...the inference task of predicting potential future events faces significant challenges, as it must deal with uncertainty, complexity, and missing data. To this end, this study proposes a new TKG extrapolation model SubRE-NET, based on the Recurrent Event Network(RE-NET). The model performs reasoning by aggregating local and global information, and introduces a subgraph in the encoding stage to enhance the ability to capture local correlations and temporal features of events within a time window. At the same time, the attention mechanism is introduced to solve the problem in which the original model cannot distinguish the importance of nodes and relationships, and the subgraph is further enhanced by cropping technology. In the aggregation stage, an extended relational graph convolutional network(RGCN) was adopted to overcome the limitations of the original model, which cannot capture temporal information when locally and globally aggregated. The experimental results show that, compared with the baseline model RE-NET, our SubRE-NET model achieved significant performance improvements on three event-based datasets and two public knowledge graph datasets, with an average MRR performance improvement of 11.49%. Simultaneously, the average performance of the Hits@1 metric is improved by 12.38%.
Photocatalysis is a new type of technology, which has been developed rapidly for solving environmental problems such as wastewater or air pollutants in recent years. Also, the effective performance ...and non-secondary pollution of photocatalytic technology attract much attention from researchers. As a “sillén” phase oxide, the (BiO)
2
CO
3
(BOC) is a great potential photocatalyst attributing to composed of alternate Bi
2
O
2
2+
and CO
3
2−
layers, which is a benefit for transportation of electrons. Besides, BOC has attracted much attention from researchers because of its excellent characters of non-toxic, environmentally friendly, and low-cost. However, BOC has a defect on wide band gap, which is limited for the usage of visible light, so a great number of published papers focus on the modifications of BOC to improve its photocatalytic efficiency. This article mainly summarizes the modifications of BOC and its application in the environment, guiding for designing BOC-based materials with high photocatalytic activity driven by light. Moreover, the research trend and prospect of BOC photocatalyst were briefly summarized, which could lay the foundation for forming a green and efficient BOC-based photocatalytic reaction system. Importantly, this review might provide a theoretical basis and guidance for further research in this field.
Graphical abstract
Given the inexorable increase in global wastewater treatment, increasing amounts of nitrous oxide are expected to be emitted from wastewater treatment plants and released to the atmosphere. It has ...become imperative to study the emission and control of nitrous oxide in the various wastewater treatment processes currently in use. In the present investigation, the emission characteristics and the factors affecting the release of nitrous oxide were studied via full- and pilot-scale experiments in anoxic-oxic, sequencing batch reactor and oxidation ditch processes. We propose an optimal treatment process and relative strategy for nitrous oxide reduction. Our results show that both the bio-nitrifying and bio-denitrifying treatment units in wastewater treatment plants are the predominant sites for nitrous oxide production in each process, while the aerated treatment units are the critical sources for nitrous oxide emission. Compared with the emission of nitrous oxide from the anoxic-oxic (1.37 % of N-influent) and sequencing batch reactor (2.69 % of N-influent) processes, much less nitrous oxide (0.25 % of N-influent) is emitted from the oxidation ditch process, which we determined as the optimal wastewater treatment process for nitrous oxide reduction, given the current technologies. Nitrous oxide emissions differed with various operating parameters. Controlling the dissolved oxygen concentration at a proper level during nitrification and denitrification and enhancing the utilization rate of organic carbon in the influent for denitrification are the two critical methods for nitrous oxide reduction in the various processes considered.
Background
As a common mental disease, depression seriously affects people’s physical and mental health. According to the statistics of the World Health Organization, depression is one of the main ...reasons for suicide and self-harm events in the world. Therefore, strengthening depression detection can effectively reduce the occurrence of suicide or self-harm events so as to save more people and families. With the development of computer technology, some researchers are trying to apply natural language processing techniques to detect people who are depressed automatically. Many existing feature engineering methods for depression detection are based on emotional characteristics, but these methods do not consider high-level emotional semantic information. The current deep learning methods for depression detection cannot accurately extract effective emotional semantic information.
Objective
In this paper, we propose an emotion-based attention network, including a semantic understanding network and an emotion understanding network, which can capture the high-level emotional semantic information effectively to improve the depression detection task.
Methods
The semantic understanding network module is used to capture the contextual semantic information. The emotion understanding network module is used to capture the emotional semantic information. There are two units in the emotion understanding network module, including a positive emotion understanding unit and a negative emotion understanding unit, which are used to capture the positive emotional information and the negative emotional information, respectively. We further proposed a dynamic fusion strategy in the emotion understanding network module to fuse the positive emotional information and the negative emotional information.
Results
We evaluated our method on the Reddit data set. The experimental results showed that the proposed emotion-based attention network model achieved an accuracy, precision, recall, and F-measure of 91.30%, 91.91%, 96.15%, and 93.98%, respectively, which are comparable results compared with state-of-the-art methods.
Conclusions
The experimental results showed that our model is competitive with the state-of-the-art models. The semantic understanding network module, the emotion understanding network module, and the dynamic fusion strategy are effective modules for depression detection. In addition, the experimental results verified that the emotional semantic information was effective in depression detection.
The intellectual properties (IP) protection of deep neural networks (DNN) models has raised many concerns in recent years. To date, most of the existing works use DNN watermarking to protect the IP ...of DNN models. However, the DNN watermarking methods can only passively verify the copyright of the model after the DNN model has been pirated, which cannot prevent piracy in the first place. In this paper, an active DNN IP protection technique against DNN piracy, called ActiveGuard, is proposed. ActiveGuard can provide active authorisation control, users' identities management, and ownership verification for DNN models. Specifically, for the first time, ActiveGuard exploits well‐crafted rare and specific adversarial examples with specific classes and confidences as users' fingerprints to distinguish authorised users from unauthorised ones. Authorised users can input their fingerprints to the DNN model for identity authentication and then obtain normal usage, while unauthorised users will obtain a very poor model performance. In addition, ActiveGuard enables the model owner to embed a watermark into the weights of the DNN model for ownership verification. Compared to the few existing active DNN IP protection works, ActiveGuard can support both users' identities identification and active authorisation control. Besides, ActiveGuard introduces lower overhead than these existing active protection works. Experimental results show that, for authorised users, the test accuracy of LeNet‐5 and Wide Residual Network (WRN) models are 99.15% and 91.46%, respectively, while for unauthorised users, the test accuracy of LeNet‐5 and WRN models are only 8.92% and 10%, respectively. Besides, each authorised user can pass the fingerprint authentication with a high success rate (up to 100%). For ownership verification, the embedded watermark can be successfully extracted, while the normal performance of DNN models will not be affected. Furthermore, it is demonstrated that ActiveGuard is robust against model fine‐tuning attack, pruning attack, and three types of fingerprint forgery attacks.
An active deep neural networks (DNN) intellectual property protection technique against DNN piracy, named ActiveGuard is proposed. ActiveGuard can provide active authorisation control, users' identities management, and ownership verification for DNN. Specifically, for the first time, ActiveGuard exploits well‐crafted rare and specific adversarial examples as users’ fingerprints to distinguish authorised users from unauthorised users. Authorised users can input their fingerprints to the DNN for identity authentication and then obtain normal usage, while unauthorised users will obtain a rather poor model performance.
Celotno besedilo
Dostopno za:
DOBA, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UILJ, UKNU, UL, UM, UPUK
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