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For this study, we investigated efficient strategies for the recovery of individual links in power grids governed by the direct current (DC) power flow model, under random link failures. Our primary objective was to explore the efficacy of recovering failed links based solely on topological network metrics. In total, we considered 13 recovery strategies, which encompassed 2 strategies based on link centrality values (link betweenness and link flow betweenness), 8 strategies based on the products of node centrality values at link endpoints (degree, eigenvector, weighted eigenvector, closeness, electrical closeness, weighted electrical closeness, zeta vector, and weighted zeta vector), and 2 heuristic strategies (greedy recovery and two-step greedy recovery), in addition to the random recovery strategy. To evaluate the performance of these proposed strategies, we conducted simulations on three distinct power systems: the IEEE 30, IEEE 39, and IEEE 118 systems. Our findings revealed several key insights: Firstly, there were notable variations in the performance of the recovery strategies based on topological network metrics across different power systems. Secondly, all such strategies exhibited inferior performance when compared to the heuristic recovery strategies. Thirdly, the two-step greedy recovery strategy consistently outperformed the others, with the greedy recovery strategy ranking second. Based on our results, we conclude that relying solely on a single metric for the development of a recovery strategy is insufficient when restoring power grids following link failures. By comparison, recovery strategies employing greedy algorithms prove to be more effective choices. [ABSTRACT FROM AUTHOR]

Network controllability and its robustness have been widely studied. However, analytical methods to calculate network controllability with respect to node in- and out-degree targeted removals are currently lacking. This paper develops methods, based on generating functions for the in- and out-degree distributions, to approximate the minimum number of driver nodes needed to control directed networks, during node in- and out-degree targeted removals. By validating the proposed methods on synthetic and real-world networks, we show that our methods work reasonably well. Moreover, when the fraction of the removed nodes is below 10% the analytical results of random removals can also be used to predict the results of targeted node removals. [ABSTRACT FROM AUTHOR]

Real-time detection of fruit targets is a key technology of the Xiaomila green pepper (Capsicum frutescens L.) picking robot. The complex conditions of orchards make it difficult to achieve accurate detection. However, most of the existing deep learning network detection algorithms cannot effectively detect Xiaomila green pepper fruits covered by leaves, branches, and other fruits in natural scenes. As detailed in this paper, the Red, Green, Blue (RGB) images of Xiaomila green pepper in the green and mature stage were collected under natural light conditions for building the dataset and an improved YOLOv5s model (YOLOv5s-CFL) is proposed to improve the efficiency and adaptability of picking robots in the natural environment. First, the convolutional layer in the Cross Stage Partial (CSP) is replaced with GhostConv, the detection speed is improved through a lightweight structure, and the detection accuracy is enhanced by adding a Coordinate Attention (CA) layer and replacing Path Aggregation Network (PANet) in the neck with Bidirectional Feature Pyramid Network (BiFPN). In the experiment, the YOLOv5s-CFL model was used to detect the Xiaomila, and the detection results were analyzed and compared with those of the original YOLOv5s, YOLOv4-tiny, and YOLOv3-tiny models. With these improvements, the Mean Average Precision (mAP) of YOLOv5s-CFL is 1.1%, 6.8%, and 8.9% higher than original YOLOv5s, YOLOv4-tiny, and YOLOv3-tiny, respectively. Compared with the original YOLOv5 model, the model size is reduced from 14.4 MB to 13.8 MB, and the running speed is reduced from 15.8 to 13.9 Gflops. The experimental results indicate that the lightweight model improves the detection accuracy and has good real-time performance and application prospects in the field of picking robots. [ABSTRACT FROM AUTHOR]

The existing zoonotic coronaviruses (CoVs) and viral genetic variants are important microbiological pathogens that cause severe disease in humans and animals. Currently, no effective broad-spectrum antiviral drugs against existing and emerging CoVs are available. The CoV main protease (Mpro) plays an essential role in viral replication, making it an ideal target for drug development. However, the structure of the Deltacoronavirus Mpro is still unavailable. Porcine deltacoronavirus (PDCoV) is a novel CoV that belongs to the genus Deltacoronavirus and causes atrophic enteritis, severe diarrhea, vomiting and dehydration in pigs. Here, we determined the structure of PDCoV Mpro complexed with a Michael acceptor inhibitor. Structural comparison showed that the backbone of PDCoV Mpro is similar to those of alpha-, beta- and gamma-CoV Mpros. The substrate-binding pocket of Mpro is well conserved in the subfamily Coronavirinae. In addition, we also observed that Mpros from the same genus adopted a similar conformation. Furthermore, the structure of PDCoV Mpro in complex with a Michael acceptor inhibitor revealed the mechanism of its inhibition of PDCoV Mpro. Our results provide a basis for the development of broad-spectrum antivirals against PDCoV and other CoVs. [ABSTRACT FROM AUTHOR]

Herein, we have verified the interaction between the functional peptides from the SARS-CoV-2 and cell membrane, and we further proved that peptides exhibit little membrane disruption. The specific amino acids (Lys, Ile, Glu, Asn, Gln, etc.) with charge or hydrophobic residues play a significant role during the functional-peptide binding to membrane. The findings could provide the hints related to viral infection and also might pave the way for development of new materials based on peptides with membrane-binding activity, which would enable functional peptides further as peptide adjuvants, in order to help deliver the cancer drug into tumor cells for the efficient tumor therapy. [ABSTRACT FROM AUTHOR]

The irrational land use patterns in the Yellow River Delta (YRD) have resulted in an imbalance in ecological stoichiometry, leading to secondary salinization and soil degradation. However, there is limited knowledge about the long-term response of soil and enzyme stoichiometry to land use. This hampers our ability to optimize land use in the YRD to alleviate nutrient limitation and thus promote ecological stoichiometric balance. We investigated the stoichiometry of soil and enzyme carbon (C), nitrogen (N), and phosphorus (P) in three land use patterns (Alfalfa artificial grassland, AG; wheat–maize rotation field, WM; native grassland, PC) established for 19 years in the YRD. The results showed that the soil stoichiometry of the three land uses in the YRD was lower than the world and Chinese averages, indicating lower C and N levels. Nutrient limitations of soil microorganisms were C and P due to an enzyme C:N ratio greater than 1:1 and vector angle greater than 45°. The three land use patterns have different advantages in alleviating nutrient limitations in the YRD. AG promotes soil macroaggregate formation, reduces soil salt content, improves nutrient availability, and mitigates N limitation. This makes AG more conducive to improving the poor soil structure, high soil salinity, and stoichiometric imbalance in the YRD to mitigate local soil degradation and be suitable for long-term continuous cultivation. WM is beneficial for increasing soil total C content due to straw return. However, WM does not reduce soil salinity. WM is more suitable for intercropping or crop rotation to improve soil C content in the YRD. Although PC can alleviate soil microbial C limitation due to its significantly lower vector length than AG and WM, the low nutrient levels hindered its ability to alleviate local soil nutrient limitation. In conclusion, our study provides a theoretical basis for rational land use in the YRD to mitigate soil degradation. [ABSTRACT FROM AUTHOR]

1,1,1-Trichloroethane (1,1,1-TCA) is a typical organochloride solvent in groundwater that poses threats to human health and the environment due to its carcinogenesis and bioaccumulation. In this study, a novel composite with nanoscale zero-valent iron (nZVI) supported by polycaprolac-tone (PCL)-modified biochar (nZVI@PBC) was synthesized via solution intercalation and liquid-phase reduction to address the 1,1,1-TCA pollution problem in groundwater. The synergy effect and improvement mechanism of 1,1,1-TCA removal from simulated groundwater in the presence of nZVI@PBC coupling with Shewanella putrefaciens CN32 were investigated. The results were as follows: (1) The composite surface was rough and porous, and PCL and nZVI were loaded uniformly onto the biochar surface as micro-particles and nanoparticles, respectively; (2) the optimal mass ratio of PCL, biochar, and nZVI was 1:7:2, and the optimal composite dosage was 1.0% (w/v); (3) under the optimal conditions, nZVI@PBC + CN32 exhibited excellent removal performance for 1,1,1-TCA, with a removal rate of 82.98% within 360 h, while the maximum removal rate was only 41.44% in the nZVI + CN32 treatment; (4) the abundance of CN32 and the concentration of adsorbed Fe(II) in the nZVI@PBC + CN32 treatment were significantly higher than that in control treatments, while the total organic carbon (TOC) concentration first increased and then decreased during the culture process; (5) the major improvement mechanisms include the nZVI-mediated chemical reductive dechlorination and the CN32-mediated microbial dissimilatory iron reduction. In conclusion, the nZVI@PBC composite coupling with CN32 can be a potential technique to apply for 1,1,1-TCA removal in groundwater. [ABSTRACT FROM AUTHOR]

Aloe vera, Mentha arvensis (mint), Coriandrum sativum (coriander), and Cymbopogon citratus (lemongrass) leaf extracts were used to synthesize stable silver nanoparticles (Ag-NPs) by green chemistry. UV–vis spectrophotometry, X-ray diffraction (XRD), thermogravimetric analysis (TGA), scanning electron microscopy (SEM), and energy dispersive X-ray (EDX) spectroscopy techniques were used to characterize these biosynthesized nanoparticles. The data indicated that the silver nanoparticles were successfully synthesized, and the narrower particle size distribution was at 10–22 nm by maintaining a specific pH. As a short-term post-sowing treatment, Ag-NP solutions of different sizes (10 and 50 ppm) were introduced to mung bean seedlings, and the overall increase in plant growth was found to be more pronounced at 50 ppm concentration. The antibacterial activity of Ag-NPs was also investigated by disc diffusion test, minimum inhibitory concentration (MIC), and minimum bactericidal concentration (MBC) test. The zones of inhibition (ZOI) were shown by Escherichia coli (E. coli) (1.9, 2.1, 1.7, and 2 mm), followed by Staphylococcus aureus (S. aureus) (1.8, 1.7, 1.6, and 1.9 mm), against coriander, mint, Aloe vera, and lemongrass, respectively. MIC and MBC values of E. coli, and S. aureus ranged from 7 to 8 µg/mL. Overall, this study demonstrates that Ag-NPs exhibit a strong antimicrobial activity and thus might be developed as a new type of antimicrobial agent for the treatment of bacterial infection. [ABSTRACT FROM AUTHOR]

In recent years, the incidence of tuberculosis (TB) and mortality caused by the disease have been decreasing. However, the number of drug-resistant tuberculosis patients is increasing rapidly year by year. Here, a total of 380 Mycobacterium tuberculosis (MTB)-positive formalin-fixed and paraffin-embedded tissue (FFPE) specimens diagnosed in the Department of Pathology of the Eighth Medical Center, Chinese PLA General Hospital were collected. Among 380 cases of MTB, 85 (22.37%) were susceptible to four anti-TB drugs and the remaining 295 (77.63%) were resistant to one or more drugs. The rate of MDR-TB was higher in previously treated cases (52.53%) than in new cases [(36.65%), p  <  0.05]. Of previously treated cases, the rate of drug resistance was higher in females than in males (p  <  0.05). Among specimens obtained from males, the rate of drug resistance was higher in new cases than in previously treated cases (p  <  0.05). Of mutation in drug resistance-related genes, the majority (53/380, 13.95%) of rpoB gene carried the D516V mutation, and 13.42% (51/380) featured mutations in both the katG and inhA genes. Among the total specimens, 18.68% (71/380) carried the 88 M mutation in the rpsL gene, and the embB gene focused on the 306 M2 mutation with a mutation rate of 19.74%. Among the resistant INH, the mutation rate of −15 M was higher in resistance to more than one drug than in monodrug-resistant (p  <  0.05). In conclusion, the drug resistance of MTB is still very severe and the timely detection of drug resistance is conducive to the precise treatment of TB. [ABSTRACT FROM AUTHOR]

The discrete wavelet transform (DWT) is unable to represent the directional features of an image. Similarly, a fixed embedding strength is not able to establish an ideal balance between imperceptibility and robustness of a watermarked image. In this work, we propose an adaptive embedding strength watermarking algorithm based on shearlets' capture directional features (S-AES). We improve the watermarking algorithm in the domain of DWT using non-subsampled shearlet transform (NSST). The improvement is made in terms of coping with anti-geometric attacks. The embedding strength is optimized by artificial bee colony (ABC) to achieve higher robustness under the premise of satisfying imperceptibility. The principle components (PC) of the watermark are embedded into the host image to overcome the false positive problem. The simulation results show that the proposed algorithm has better imperceptibility and strong robustness against multi-attacks, especially those of high intensity. [ABSTRACT FROM AUTHOR]

Barium titanate (BT)-based ceramics are one of the promising piezoelectric materials for environment-friendly electro-mechanical transformation. However, high performance materials are often sintered at high temperatures, resulting in volatile components and increased energy consumption. Here, 0.82Ba(Ti0.89Sn0.11)O3-(0.18-x)(Ba0.7Ca0.3)TiO3-xBiFeO3 (BTS-BCT-xBF) piezoelectric ceramics were prepared by microwave sintering (MWS) method, and the structure and properties were emphatically studied, aiming to reveal the regulatory mechanism of MWS on the structure and properties. Compared with conventional solid sintering (CS), the phase structure presents a similar evolution in MWS ceramics as a function of BF, while the more refined grain size and the denser structure are observed in MWS ceramics. The electrical properties (e.g., d33, εr, tan δ, etc.) of MWS ceramics are superior to the CS ceramics owing to the refined grain size and denser microstructure. It is worth noting that the energy storage performance (e.g., energy storage density, energy storage efficiency) significantly outperformed expectations due to the slender hysteresis loop resulting from the smaller grain and high cubic phase. Therefore, the MWS sintering mechanism can further drive practical application of BT-based ceramics. [ABSTRACT FROM AUTHOR]