Layered microporous polymers by solvent knitting method

Two-dimensional (2D) nanomaterials, especially 2D organic nanomaterials with unprecedentedly diverse and controlled structure, have attracted decent scientific interest. Among the preparation strategies, the top-down approach is one of the considered low-cost and scalable strategies to obtain 2D organic nanomaterials. However, some factors of their layered counterparts limited the development and potential applications of 2D organic nanomaterials, such as type, stability, and strict synthetic conditions of layered counterparts. We report a class of layered solvent knitting hyper-cross-linked microporous polymers (SHCPs) prepared by improving Friedel-Crafts reaction and using dichloroalkane as an economical solvent, stable electrophilic reagent, and external cross-linker at low temperature, which could be used as layered counterparts to obtain previously unknown 2D SHCP nanosheets by method of ultrasonic-assisted solvent exfoliation. This efficient and low-cost strategy can produce previously unreported microporous organic polymers with layered structure and high surface area and gas storage capacity. The pore structure and surface area of these polymers can be controlled by tuning the chain length of the solvent, the molar ratio of AlCl(3), and the size of monomers. Furthermore, we successfully obtain an unprecedentedly high–surface area HCP material (3002 m(2) g(−1)), which shows decent gas storage capacity (4.82 mmol g(−1) at 273 K and 1.00 bar for CO(2); 12.40 mmol g(−1) at 77.3 K and 1.13 bar for H(2)). This finding provides an opportunity for breaking the constraint of former knitting methods and opening up avenues for the design and synthesis of previously unknown layered HCP materials

TRNet: Two-level Refinement Network leveraging Speech Enhancement for Noise Robust Speech Emotion Recognition

One persistent challenge in Speech Emotion Recognition (SER) is the ubiquitous environmental noise, which frequently results in deteriorating SER performance in practice. In this paper, we introduce a Two-level Refinement Network, dubbed TRNet, to address this challenge. Specifically, a pre-trained speech enhancement module is employed for front-end noise reduction and noise level estimation. Later, we utilize clean speech spectrograms and their corresponding deep representations as reference signals to refine the spectrogram distortion and representation shift of enhanced speech during model training. Experimental results validate that the proposed TRNet substantially promotes the robustness of the proposed system in both matched and unmatched noisy environments, without compromising its performance in noise-free environments.14 pages, 3 figure

Simulation of Fragmentation Characteristics of Projectile Jacket Made of Tungsten Alloy after Penetrating Metal Target Plate using SPH Method

A smooth particle hydrodynamics (SPH) model was used to simulate the fragmentation process of the jacket during penetrator with lateral efficiency (PELE) penetrating the metal target plate to study the fragmentation characteristics of PELE jacket made of tungsten alloy. The validity of the SPH model was verified by experimental results. Then the SPH model was used to simulate the jacket fragmentation under different impact velocity and thickness of target plate. The influence of impact velocity and thickness of target plate on the jacket fragmentation was obtained by analysing the mass distribution and quantity distribution of the fragments formed by the jacket. The results show that the dynamic fragmentation of tungsten alloy can be simulated effectively using the SPH model, Johnson-Cook strength model, maximum tensile stress failure criterion and stochastic failure model. When the thickness of target plate is fixed, the greater the impact velocity, the greater the pressure produced by the projectile impacting the target plate; with the increase of impact velocity, the mass of residual projectile decreases, the number of fragments formed by fragmentation of jacket increases linearly, and the average mass of fragments decreases exponentially. When the impact velocity is constant, the greater the thickness of the target plate, the longer the pressure duration by the projectile impacting the target plate; with the increase of the thickness of target plate, the mass of residual projectile decreases, the number of fragments formed by fragmentation of jacket increases linearly, and the average mass of fragments decreases exponentially. The numerical calculation model and research method adopted in this paper can be used to study the impact fragmentation of solid materials effectively

Fragmentation Behaviour of Radial Layered PELE Impacting Thin Metal Target Plates

The fragmentation mechanism of the penetrator with lateral effect (PELE) after perforating a thin target plate has been summarised and analysed firstly. Then the fragmentation of radial layered PELE was analysed qualitatively and verified by experiment. In the experiment, the target plates were made of 45# steel and 2A12 aluminium respectively. Qualitative analysis and experimental results show that: for normal PELE without layered, after perforating the thin metal target plate, from the bottom to the head of the projectile, the number of fragments formed by the jacket gradually increases, and the mass of the fragment decreases correspondingly. Compared with the normal PELE without layered, the radial layered PELE is less likely to break into fragments, when impacting the thin metal target plate with the same material and thickness under the same impact velocity. However, from the mechanism of the PELE, when the resistance of the target plate is large enough, and the duration of pressure is long enough, the radial layered PELE also can break into fragments with transverse velocity component. The resistance of the target plate plays an important role in the fragmentation of radial layered PELE. The radial layered PELE produced massive fragments with transverse velocity component when impacting the 45# steel plate with5 mm thickness under the impact velocity of 657.2 m/s

DBDH: A Dual-Branch Dual-Head Neural Network for Invisible Embedded Regions Localization

Embedding invisible hyperlinks or hidden codes in images to replace QR codes has become a hot topic recently. This technology requires first localizing the embedded region in the captured photos before decoding. Existing methods that train models to find the invisible embedded region struggle to obtain accurate localization results, leading to degraded decoding accuracy. This limitation is primarily because the CNN network is sensitive to low-frequency signals, while the embedded signal is typically in the high-frequency form. Based on this, this paper proposes a Dual-Branch Dual-Head (DBDH) neural network tailored for the precise localization of invisible embedded regions. Specifically, DBDH uses a low-level texture branch containing 62 high-pass filters to capture the high-frequency signals induced by embedding. A high-level context branch is used to extract discriminative features between the embedded and normal regions. DBDH employs a detection head to directly detect the four vertices of the embedding region. In addition, we introduce an extra segmentation head to segment the mask of the embedding region during training. The segmentation head provides pixel-level supervision for model learning, facilitating better learning of the embedded signals. Based on two state-of-the-art invisible offline-to-online messaging methods, we construct two datasets and augmentation strategies for training and testing localization models. Extensive experiments demonstrate the superior performance of the proposed DBDH over existing methods.Comment: 7 pages, 6 figures (Have been accepted by IJCNN 2024

Whole-genome shotgun sequencing unravels the influence of environmental microbial co-infections on the treatment efficacy for severe pediatric infectious diseases

BackgroundThe microbiome plays a pivotal role in mediating immune deviation during the development of early-life viral infections. Recurrent infections in children are considered a risk factor for disease development. This study delves into the metagenomics of the microbiome in children suffering from severe infections, seeking to identify potential sources of these infections.AimsThe aim of this study was to identify the specific microorganisms and factors that significantly influence the treatment duration in patients suffering from severe infections. We sought to understand how these microbial communities and other variables may affect the treatment duration and the use of antibiotics of these patients with severe infections.MethodWhole-genome shotgun sequencing was conducted on samples collected from children aged 0–14 years with severe infections, admitted to the Pediatrics Department of Xiamen First Hospital. The Kraken2 algorithm was used for taxonomic identification from sequence reads, and linear mixed models were employed to identify significant microorganisms influencing treatment duration. Colwellia, Cryptococcus, and Citrobacter were found to significantly correlate with the duration of clinical treatment. Further analysis using propensity score matching (PSM) and rank-sum test identified clinical indicators significantly associated with the presence of these microorganisms.ResultsUsing a linear mixed model after removed the outliers, we identified that the abundance of Colwellia, Cryptococcus, and Citrobacter significantly influences the treatment duration. The presence of these microorganisms is associated with a longer treatment duration for patients. Furthermore, these microorganisms were found to impact various clinical measures. Notably, an increase in hospitalization durations and medication costs was observed in patients with these microorganisms. In patients with Colwellia, Cryptococcus, and Citrobacter, we discover significant differences in platelets levels. We also find that in patients with Cryptococcus, white blood cells, hemoglobin, and neutrophils levels are lower.ConclusionThese findings suggest that Colwellia, Cryptococcus, and Citrobacter, particularly Cryptococcus, could potentially contribute to the severity of infections observed in this cohort, possibly as co-infections. These microorganisms warrant further investigation into their pathogenic roles and mechanisms of action, as their presence in combination with disease-causing organisms may have a synergistic effect on disease severity. Understanding the interplay between these microorganisms and pathogenic agents could provide valuable insights into the complex nature of severe pediatric infections and guide the development of targeted therapeutic strategies