In this paper, the event-triggered semiglobal consensus problem is investigated for general linear multi-agent systems subjected to input saturation, by utilizing the algebraic Riccati equation-based ...low-gain feedback technique. Two scenarios for systems with or without updating delays are considered, and fully distributed event-triggered control schemes are proposed to guarantee the semiglobal consensus of the connected systems, in which each agent is asymptotically null controllable with bounded controls. Strictly positive lower bounds for both the sampling intervals and the updating delays are captured for each agent to eliminate the Zeno behaviors in these two event-triggered processes. Finally, the effectiveness of these event-triggered control schemes are verified by simulations.
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•Optimized strategies of stable operation for anammox process were summarized.•Applying granular sludge and biofilms favors retention of anammox biomass.•Achieving well performance by ...coordinating the activity of AOB and AnAOB.•Restraining NOB to avoid the deterioration of nitrogen removal performance.•Competition, synergy and cooperation between denitrification and anammox.
The one-stage nitritation/anammox (anaerobic ammonium oxidation) process is an energy-saving technology, which has been successfully developed and widely applied to treat industrial wastewaters. For the one-stage nitritation/anammox process, key functional microbes generally include anaerobic ammonia oxidation bacteria (AnAOB), ammonia-oxidizing bacteria (AOB), nitrite oxidizing bacteria (NOB), and heterotrophic bacteria (HB). Cooperation and competition among the key functional microbes are critical to the stability and performance of anammox process. Based upon key functional microorganisms, this review summarizes and discusses the optimized strategies that promote the operation of one-stage nitritation/anammox process. In particular, the review focuses on strategies related to: (1) the retention of anammox biomass through granular sludge or biofilm, (2) the balanced relationship between AOB and AnAOB, (3) the NOB suppression and (4) the HB management by controlling the influent organic matter. In addition, the review proposes further research to address the existing challenges.
•A weakly supervised semantic segmentation network for crack detection is proposed.•The proposed patch-based method can be flexibly applied to images of different sizes.•The proposed method can ...significantly reduce the annotation workload of the semantic segmentation method.
Obtaining spatial and topological information for cracks in construction materials is important for the evaluation of service performance in infrastructure engineering. Manually extracting crack information from an image is a tedious process. Recently, popular deep learning-based semantic segmentation technologies have been employed to alleviate this problem. However, existing semantic segmentation methods for crack detection are fully supervised, i.e., these methods require manual annotation of data to obtain pixel-level labels for training, which is time-consuming. To solve this problem, this paper proposes a patch-based weakly supervised semantic segmentation network for crack detection. The proposed method uses image-level annotation as the supervision condition and fully considers the local similarity of the crack topology in the image. The use of patches cropped from the image as the input in this method can reduce the image complexity significantly without losing the spatial location information of the crack. A discriminative localization technique is used to extract rough location information of the crack from a trained classification network, which is then refined by a conditional random field to obtain a synthetic label. These synthetic labels can replace the manually annotated pixel-level labels for the training of the segmentation network. Thereafter, a neighborhood fusion strategy is used to merge the patches into the final output. Two datasets are employed to train and evaluate the proposed method. The results indicate that this method can achieve a performance comparable to that of fully supervised methods (an MIoU of 0.821 and F-score of 0.8 were obtained for the fully supervised method compared with an MIoU of 0.782 and F-score of 0.741 with the proposed method), while reducing the annotation workload by approximately 80%.
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•Nanosecond pulsed DBD with different parameters was applied in CH4 dry reforming.•Discharge, conversion characteristics and emission spectrum were analyzed.•The detected maximum ...CH4&CO2 conversions were 39.6% and 22.9%, separately.•There were higher conversion and efficiency with a shorter rise time.
Dry reforming is a promising approach to converting CH4 and CO2 (i.e., two common greenhouse gases) into clean fuels and valuable chemicals. Non-thermal plasma, acting as an alternative to the traditional reforming processes, achieves considerable gas conversion with low energy consumption under mild operating conditions. In this study, CH4 and CO2 were converted to syngas (i.e., H2 and CO) in a nanosecond pulsed dielectric barrier discharge plasma at a total gas flow rate of 50 sccm. Through evaluating the effects of electrical parameters on reforming performance, the experimental results showed that CH4 and CO2 conversions increased with the increase of pulse repetition frequency owing to the increased energy injection. Shorter rise and fall times resulted in better CH4 and CO2 conversions and higher energy conversion efficiencies, due to the rapid acceleration of electrons in a shorter discharge time. In the case where the optimal pulse peak width was 150 ns, the secondary discharge was improved because of the charge accumulation in the primary discharge, thereby increasing the CH4 and CO2 conversions. Among all experiments, when the pulse repetition frequency was 10 kHz and the discharge power was 55.7 W, the maximum conversions of CH4 and CO2 were 39.6% and 22.9%, respectively, while the total energy conversion efficiencies of the syngas and all detected products were 5.0% and 7.1%, respectively. Furthermore, an optical emission spectroscopy was used to characterize the active species formed during the reforming process.
The utility of CRISPR-Cas9 and TALENs for genome editing may be compromised by their off-target activity. We show that integrase-defective lentiviral vectors (IDLVs) can detect such off-target ...cleavage with a frequency as low as 1%. In the case of Cas9, we find frequent off-target sites with a one-base bulge or up to 13 mismatches between the single guide RNA (sgRNA) and its genomic target, which refines sgRNA design.
•A hybrid 1D-CNN and attention-based Bi-GRU model is developed for moisture content prediction.•The proposed model can simultaneously extract spectral local abstract information and position ...information.•The proposed model shows superior performance on both LUCAS and sand gravel spectral datasets.•A moisture content characteristic wavelength (CW) screening process is established.•The top ten CW points are calculated to help realize low-cost discrete NIR spectrometer.
A non-destructive and rapid moisture content detection method of sand gravel material is required in loose material dams. The near-infrared (NIR) spectrum of sand materials is closely related to its moisture content. Recently, there is a growing need for fully using spectral information when establishing calibration models for sand gravel moisture content detection. To address these issues, a hybrid one dimensional-convolutional neural network (1D-CNN) and attention-based bidirectional gated recurrent unit (Bi-GRU) neural network was proposed to detect sand gravel moisture content with NIR spectrum. Two learners, namely, 1D-CNN and Bi-GRU, were constructed to extract local abstract information and sequence position information from the spectrum, respectively. In the 1D-CNN learner, multiple kernels CNN layers and one dimensional-separable convolution layers were conjunct to improve model accuracy and reduce network parameters. In the Bi-GRU learner, a multi-head self-attention mechanism was appended to evaluate the weights of the output features extracted by Bi-GRU layers. The proposed model achieved the best prediction results in LUCAS dataset (R2 greater than 0.75, RPD greater than 2.0) and our sand gravel spectral dataset (R2 = 0.96, RPD = 5.06) compared to other deep learning and conventional spectroscopy analysis methods. In addition, the top ten characteristic wavelength points of sand gravel were identified. These can be used to choose a discrete spectrum measuring instrument, which has a relatively low cost.
C1q/tumor necrosis factor (TNF)-related protein 12 (CTRP12) plays a crucial part in cardiovascular diseases especially the coronary artery disease. Nonetheless, it is unrevealed that whether the ...CTRP12 participates in the progress of cardiac fibrosis. In this study, we investigated whether CTRP12 regulates pathological myocardial fibrosis. We isolated neonatal rat cardiac fibroblasts were cultured with recombination CTRP12 followed by stimulating with Isoproterenol (ISO, 100 µM) for 24 h. Then the adenovirus were used to achieve the CTRP12-overexpressed fibroblasts. In vivo, the C57/B6 mice were subjected to recombinant human CTRP12 (0.2 µg/g/d) for 2 weeks after injected with Isoproterenol (ISO, 10 mg/kg/d for 3 d then 5 mg/kg/d for 11 d, subcutaneously (s.c.), 2 weeks) and mice were also subjected to adenovirus with P38 overexpressing system to explore the mechanism. As a result, CTRP12 significantly inhibit the transformation of cardiac fibroblasts to myofibroblasts and the transcription of cardiac fibrosis-related proteins induced by ISO in vitro. The administration of CTRP12 can effectively reduce the cardiac fibrosis and enhance the cardiac function in mice hearts. The treatment with CTRP12 did not change the expression level of phosphorylated (p)-smad2, smad4, p-extracellular regulated protein kinases 1/2 and c-Jun N-terminal kinase 1/2, but it suppressed the activation of p38. Cardiac overexpression of p38 could abolish this kind of cardioprotective effects by CTRP12. In summary, the CTRP12 protect against the ISO induced cardiac fibrosis via suppressing the p38 signal pathway.
Bitter tastes are innately aversive and are thought to help protect animals from consuming poisons. Children are extremely sensitive to drug tastes, and their compliance is especially poor with ...bitter medicine. Therefore, judging whether a drug is bitter and adopting flavor correction and taste-masking strategies are key to solving the problem of drug compliance in children. Although various machine learning models for bitterness and sweetness prediction have been reported in the literature, no learning model or bitterness database for children’s medication has yet been reported. In this study, we trained four different machine learning models to predict bitterness. The goal of this study was to develop and validate a machine learning model called the “Children’s Bitter Drug Prediction System” (CBDPS) based on Tkinter, which predicts the bitterness of a medicine based on its chemical structure. Users can enter the Simplified Molecular-Input Line-Entry System (SMILES) formula for a single compound or multiple compounds, and CBDPS will predict the bitterness of children’s medicines made from those XGBoost–Molecular ACCess System (XgBoost–MACCS) model yielded an accuracy of 88% under cross-validation.
Antidiabetic medication may modulate the gut microbiota and thereby alter plasma and faecal bile acid (BA) composition, which may improve metabolic health. Here we show that treatment with Acarbose, ...but not Glipizide, increases the ratio between primary BAs and secondary BAs and plasma levels of unconjugated BAs in treatment-naive type 2 diabetes (T2D) patients, which may beneficially affect metabolism. Acarbose increases the relative abundances of Lactobacillus and Bifidobacterium in the gut microbiota and depletes Bacteroides, thereby changing the relative abundance of microbial genes involved in BA metabolism. Treatment outcomes of Acarbose are dependent on gut microbiota compositions prior to treatment. Compared to patients with a gut microbiota dominated by Prevotella, those with a high abundance of Bacteroides exhibit more changes in plasma BAs and greater improvement in metabolic parameters after Acarbose treatment. Our work highlights the potential for stratification of T2D patients based on their gut microbiota prior to treatment.
Engineering multifunctional nanocarriers for targeted drug delivery shows promising potentials to revolutionize the cancer chemotherapy. Simple methods to optimize physicochemical characteristics and ...surface composition of the drug nanocarriers need to be developed in order to tackle major challenges for smooth translation of suitable nanocarriers to clinical applications. Here, rational development and utilization of multifunctional mesoporous silica nanoparticles (MSNPs) for targeting MDA‐MB‐231 xenograft model breast cancer in vivo are reported. Uniform and redispersible poly(ethylene glycol)‐incorporated MSNPs with three different sizes (48, 72, 100 nm) are synthesized. They are then functionalized with amino‐β‐cyclodextrin bridged by cleavable disulfide bonds, where amino‐β‐cyclodextrin blocks drugs inside the mesopores. The incorporation of active folate targeting ligand onto 48 nm of multifunctional MSNPs (PEG‐MSNPs48‐CD‐PEG‐FA) leads to improved and selective uptake of the nanoparticles into tumor. Targeted drug delivery capability of PEG‐MSNPs48‐CD‐PEG‐FA is demonstrated by significant inhibition of the tumor growth in mice treated with doxorubicin‐loaded nanoparticles, where doxorubicin is released triggered by intracellular acidic pH and glutathione. Doxorubicin‐loaded PEG‐MSNPs48‐CD‐PEG‐FA exhibits better in vivo therapeutic efficacy as compared with free doxorubicin and non‐targeted nanoparticles. Current study presents successful utilization of multifunctional MSNP‐based drug nanocarriers for targeted cancer therapy in vivo.
Biocompatible, uniform, and redispersible mesoporous silica nanoparticles are developed for cancer‐targeted drug delivery in vivo. The folate‐functionalized mesoporous silica nanoparticles with a core diameter of 48 nm can deliver sufficient amount of doxorubicin into tumor, resulting in a remarkable tumor‐inhibiting effect as compared with those of free doxorubicin and non‐targeted nanoparticles.
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