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This study presents a novel approach in the realm of catalytic organic synthesis by integrating biomass catalytic conversion with organic synthesis techniques. Utilizing N-acetylglucosamine as the primary feedstock, the first phase of the research involves its catalytic transformation into 5-hydroxymethylfurfural (HMF). The subsequent phase employs a condensation reaction between HMF and 3,3-Dimethyl-2-butanone to synthesize a new compound, (E)-1-(5-(hydroxymethyl) furan-2-yl)-4,4-dimethylpent-1-en-3-one. This two-step process not only demonstrates the feasibility of converting biomass into valuable chemical precursors but also exemplifies the synthesis of novel compounds through green chemistry principles. The successful execution of this methodology offers fresh insights and opens new avenues for advancements in catalytic organic synthesis, emphasizing sustainability and efficiency.

Residual stresses with a complex distribution are generated after linear friction welding, which affects the service performance of the weldment. In this work, a numerical model for linear-friction-welded Ti17 (Ti-5Al-2Sn-2Zr-4Mo-4Cr) was developed to investigate the evolution of residual stresses and the effect of welding parameters on residual stresses. Additionally, a method for predicting internal residual stresses was constructed. The results indicate that the residual stresses near the contact interface are largest in the oscillatory direction, peaking at ~661 MPa at 2 mm away from the contact interface. The evolution of stresses is not only related to the inhomogeneous thermal gradient, but also to the forging force. And the stress distribution essentially stabilizes within the duration of the forging force applied. Increasing the amplitude and frequency results in higher peaks of tensile residual stresses and a more concentrated distribution. Conversely, increasing the forging force only reduces the magnitude of the residual stresses. The developed prediction method, based on the similarity of internal residual stress distributions, facilitates the prediction of internal residual stresses using measured surface residual stresses.

To investigate the contents of carbon, nitrogen, and phosphorus in tea plantation soils and their ecological stoichiometric characteristics, as well as their response to environmental factors in high-latitude regions of China, soil samples from 0 to 20 cm depth were collected from tea plantations at different altitudes and cultivation years in the main tea-producing areas of Shaanxi Province. These samples were used to determine the soil organic carbon (SOC), total nitrogen (TN), and total phosphorus (TP) contents and to calculate their stoichiometric ratios. The findings revealed the following: the average soil SOC and TN content in tea gardens were 13.15 and 1.30 g·kg−1, respectively, exceeding the national soil average. These values met the Class I tea garden fertility standards. However, the average soil TP content, at 0.45 g·kg−1, fell below the national soil average, meeting the Class II tea garden fertility standards. In tea gardens, the average ratios of carbon to nitrogen (C:N), carbon to phosphorus (C:P), and nitrogen to phosphorus (N:P) in the soil were 10.42, 30.98, and 3.32, respectively. These ratios were all lower than the national soil average, indicating relatively high phosphorus availability but nitrogen deficiency in tea garden soils. As altitude increased, there was a decline in soil SOC content, C N, and C P ratios, followed by a subsequent increase. No significant changes were seen in TN, TP, and N P ratio in the soil, but there was an increase in SOC content, TN content, and C P ratio during cultivation. The N-to-P ratio initially increased before decreasing, while the C-to-N ratio decreased before increasing. Soil TP content did not change significantly. The study recommends careful nitrogen fertilizer application in tea garden management to balance nitrogen and phosphorus.

With the swift advancement of wearable electronics and artificial intelligence, the integration of electronic devices with the human body has advanced significantly, leading to enhanced real-time health monitoring and remote disease diagnosis. Despite progress in developing stretchable materials with skin-like mechanical properties, there remains a need for materials that also exhibit high optical transparency. Supercapacitors, as promising energy storage devices, offer advantages such as portability, long cycle life, and rapid charge/discharge rates, but achieving high capacity, stretchability, and transparency simultaneously remains challenging. This study combines the stretchable, transparent polymer PEDOT:PSS with MnO2 nanoparticles to develop high-performance, stretchable, and transparent supercapacitors. PEDOT:PSS films were deposited on a PDMS substrate using a spin-coating method, followed by electrochemical deposition of MnO2 nanoparticles. This method ensured that the nanosized MnO2 particles were uniformly distributed, maintaining the transparency and stretchability of PEDOT:PSS. The resulting PEDOT:PSS/MnO2 nanoparticle electrodes were gathered into a symmetric device using a LiCl/PVA gel electrolyte, achieving an areal capacitance of 1.14 mF cm−2 at 71.2% transparency and maintaining 89.92% capacitance after 5000 cycles of 20% strain. This work presents a scalable and economical technique to manufacturing supercapacitors that combine high capacity, transparency, and mechanical stretchability, suggesting potential applications in wearable electronics.

The issue of uneven ground settlement caused by the excavation of subway tunnels represents a significant challenge in the design and construction of subway projects. This paper examined the fractal characteristics of surface settlement caused by subway excavation, employing wavelet transform and fractal theory. Firstly, the noise reduction effects of different wavelet functions, decomposition levels, threshold functions, and threshold selection rules were evaluated using the SNR and RMSE. Subsequently, 291 data points were derived from 18 interpolation points through fractal interpolation, representing a utilization of only 18% of the original data, to enhance the original monitoring data information by a factor of 2.94. The average relative error between the fractal interpolation results and the monitoring data was approximately 13%, which was indicative of a high degree of accuracy. Finally, the fractal dimension of the monitoring curves under different monitoring frequencies was calculated using the box-counting method. The denoising effect of the dbN wavelet function was found to be superior to that of the symN wavelet function in the denoising process of subway construction surface deformation monitoring data. Furthermore, the hard threshold method was observed to be more effective than the soft threshold method. As the monitoring frequency decreased, the fractal dimension of the deformation curves showed an overall decreasing trend. This indicated that high-frequency monitoring could capture more details and complexity of the surface settlement, while low-frequency monitoring led to a gradual flattening of the curves and a decrease in details.

Environmental pollution caused by organic effluents emitted by industry has become a worldwide issue and poses a serious threat to the public and the ecosystem. Metal–organic frameworks (MOFs), comprising metal-containing clusters and organic bridging ligands, are porous and crystalline materials, possessing fascinating shape and size-dependent properties such as high surface area, abundant active sites, well-defined crystal morphologies, and huge potential for surface functionalization. To date, numerous well designated MOFs have emerged as critical functional materials to solve the growing challenges associated with water environmental issues. Here we present the recent progress of MOF-based materials and their applications in the treatment of organic effluents. Firstly, several traditional and emerging synthesis strategies for MOF composites are introduced. Then, the structural and functional regulations of MOF composites are presented and analyzed. Finally, typical applications of MOF-based materials in treating organic effluents, including chemical, pharmaceutical, textile, and agricultural wastewaters are summarized. Overall, this review is anticipated to tailor design and regulation of MOF-based functional materials for boosting the performance of organic effluent remediation.

In the evolving landscape of portable electronics, there is a critical demand for components that meld stretchability with optical transparency, especially in supercapacitors. Traditional materials fall short in harmonizing conductivity, stretchability, transparency, and capacity. Although poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) stands out as an exemplary candidate, further performance enhancements are necessary to meet the demands of practical applications. This study presents an innovative and effective method for enhancing electrochemical properties by homogeneously incorporating Ru(III) into PEDOT:PSS. These Ru(III) PEDOT:PSS complexes are readily synthesized by dipping PEDOT:PSS films in RuCl3 solution for no longer than one minute, leveraging the high specific capacitance of Ru(III) while minimizing interference with transmittance. The supercapacitor made with this Ru(III) PEDOT:PSS complex demonstrated an areal capacitance of 1.62 mF cm−2 at a transmittance of 73.5%, which was 155% higher than that of the supercapacitor made with PEDOT:PSS under comparable transparency. Notably, the supercapacitor retained 87.8% of its initial capacitance even under 20% tensile strain across 20,000 cycles. This work presents a blueprint for developing stretchable and transparent supercapacitors, marking a significant stride toward next-generation wearable electronics.

A novel H-shaped miniplate (HSM) was specifically designed for restorative laminoplasties to restore patients’ posterior elements after laminectomies. A validated finite element (FE) model of L2/4 was utilized to create a laminectomy model, as well as three restorative laminoplasty models based on the fixation of different miniplates after a laminectomy (the RL-HSM model, the RL-LSM model, and the RL-THM model). The biomechanical effects of motion and displacement on a laminectomy and restorative laminoplasty with three different shapes for the fixation of miniplates were compared under the same mechanical conditions. This study aimed to validate the biomechanical stability, efficacy, and feasibility of a restorative laminoplasty with the fixation of miniplates post laminectomy. The laminectomy model demonstrated the greatest increase in motion and displacement, especially in axial rotation, followed by extension, flexion, and lateral bending. The restorative laminoplasty was exceptional in preserving the motion and displacement of surgical segments when compared to the intact state. This preservation was particularly evident in lateral bending and flexion/extension, with a slight maintenance efficacy observed in axial rotation. Compared to the laminectomy model, the restorative laminoplasties with the investigated miniplates demonstrated a motion-limiting effect for all directions and resulted in excellent stability levels under axial rotation and flexion/extension. The greatest reduction in motion and displacement was observed in the RL-HSM model, followed by the RL-LSM model and then the RL-THM model. When comparing the fixation of different miniplates in restorative laminoplasties, the HSMs were found to be superior to the LSMs and THMs in maintaining postoperative stability, particularly in axial rotation. The evidence suggests that a restorative laminoplasty with the fixation of miniplates is more effective than a conventional laminectomy due to the biomechanical effects of restoring posterior elements, which helps patients regain motion and limit load displacement responses in the spine after surgery, especially in axial rotation and flexion/extension. Additionally, our evaluation in this research study could benefit from further research and provide a methodological and modeling basis for the design and optimization of restorative laminoplasties.

Graphene has achieved mass production via various preparative routes and demonstrated its uniqueness in many application fields for its intrinsically high electron mobility and thermal conductivity. However, graphene faces limitations in assembling macroscopic structures because of its hydrophobic property. Therefore, balancing high crystal quality and good aqueous dispersibility is of great importance in practical applications. Herein, we propose a tape-wrapping strategy to electrochemically fabricate water-dispersible graphene (w-Gr) with both excellent dispersibility (~4.5 mg/mL, stable over 2 months), and well-preserved crystalline structure. A large production rate (4.5 mg/min, six times faster than previous electrochemical methods), high yield (65.4% ≤5 atomic layers) and good processability are demonstrated. A mechanism investigation indicates that the rational design of anode configuration to ensure proper oxidation, deep exfoliation and unobstructed mass transfer is responsible for the high efficiency of this strategy. This simple yet efficient electrochemical method is expected to promote the scalable preparation and applications of graphene.

A critical precondition for realizing mechanized transplantation in rice cultivation is the implementation of seedling tray techniques. To augment the efficacy of seeding, a precise evaluation of the quality of rice seedling cultivation in these trays is imperative. This research centers on the analysis of rice seedling tray images, employing deep learning as the foundational technology. The aim is to construct a computational model capable of autonomously evaluating seeding quality within the ambit of intelligent seedling cultivation processes. This study proposes a virtual grid-based image segmentation preprocessing method. It involves dividing the complete image of a rice seedling tray into several grid images. These grid images are then classified and marked using an improved ResNet50 model that integrates the SE attention mechanism with the Adam optimizer. Finally, the objective of detecting missing seeding areas is achieved by reassembling the marked grid images. The experimental results demonstrate that the improved ResNet50 model, integrating the SE attention mechanism and employing an initial learning rate of 0.01 over 50 iterations, attains a test set accuracy of 95.82%. This accuracy surpasses that of the AlexNet, DenseNet, and VGG16 models by respective margins of 4.55%, 2.07%, and 2.62%. This study introduces an innovative model for the automatic assessment of rice seeding quality. This model is capable of rapidly evaluating the seeding quality during the seedling phase; precisely identifying the locations of missing seeds in individual seedling trays; and effectively calculating the missing seed rate for each tray. Such precision in assessment is instrumental for optimizing seedling processes

Brazing a SiO2f/SiO2 composite with metals is often faced with two problems: poor wettability with the brazing alloy and high residual stress in the joint. To overcome these problems, we report a combined method of selective etching and depositing reduced graphene oxide (rGO) on the surface of a SiO2f/SiO2 composite (3D-rGO-SiO2f/SiO2) to assist brazing with TC4. After the combined treatment, a “3D-rGO” buffer layer formed on the surface layer of the SiO2f/SiO2, and the contact angle was reduced from 130° to 38°, which meant the wettability of active brazing alloy on the surface of SiO2f/SiO2 was obviously improved. In addition, the “3D-rGO” buffer layer contributed to fully integrating the brazing alloy and SiO2f/SiO2; then, the infiltration of the brazing alloy into the surface layer of the SiO2f/SiO2 was enhanced and formed the reduced graphene oxide with a pinning structure in the three dimensional (“3D-pinning-rGO”) structure. Moreover, the joining area of the brazing alloy and SiO2f/SiO2 was expanded and the mismatch degree between the SiO2f/SiO2 and TC4 was reduced, which was achieved by the “3D-pinning-rGO” structure. Furthermore, the concentration of the residual stress in the SiO2f/SiO2-TC4 joints transferred from the SiO2f/SiO2 to the braided quartz fibers, and the residual stress reduced from 142 MPa to 85 MPa. Furthermore, the 3D-pinning-rGO layer facilitated the transfer of heat between the substrates during the brazing process. Finally, the shear strength of the SiO2f/SiO2-TC4 joints increased from 12.5 MPa to 43.7 MPa by the selective etching and depositing rGO method.

Lakes are an important component of global water resources. In order to achieve accurate lake extractions on a large scale, this study takes the Tibetan Plateau as the study area and proposes an Automated Lake Extraction Workflow (ALEW) based on the Google Earth Engine (GEE) and deep learning in response to the problems of a low lake identification accuracy and low efficiency in complex situations. It involves pre-processing massive images and creating a database of examples of lake extraction on the Tibetan Plateau. A lightweight convolutional neural network named LiteConvNet is constructed that makes it possible to obtain spatial–spectral features for accurate extractions while using less computational resources. We execute model training and online predictions using the Google Cloud platform, which leads to the rapid extraction of lakes over the whole Tibetan Plateau. We assess LiteConvNet, along with thresholding, traditional machine learning, and various open-source classification products, through both visual interpretation and quantitative analysis. The results demonstrate that the LiteConvNet model may greatly enhance the precision of lake extraction in intricate settings, achieving an overall accuracy of 97.44%. The method presented in this paper demonstrates promising capabilities in extracting lake information on a large scale, offering practical benefits for the remote sensing monitoring and management of water resources in cloudy and climate-differentiated regions.

Tensile, compressive-shear, and tensile-shear failure are three typical failure modes of rock- and lining-support structural materials in underground engineering. Comparative studies of the acoustic emission (AE) evolution characteristics of specimens of the same shape and size under different stress states are of great significance in determining universal disaster warning guidelines. Based on a self-developed multi-functional test system, direct tensile, compressive-shear, and tensile-shear tests were conducted on intact and jointed rock-like specimens, comparing AE amplitude and peak frequency parameters under different failure modes from the perspectives of crack scale and crack type evolution. All failure tests were monitored with microcrack propagation at an early stage until ultimate rupture occurred at the peak-load moment. As a result, the “quiet-period” could only be observed from the compressive-shear test. The AE signals distributed in three bands can be used as an indicator of failure identifications. Tensile and shear cracks can be identified by strong and weak amplitudes of low- and high-frequency signals. These results enhance the knowledge of the failure modes of rock mechanics for more applications in monitoring disasters in rock engineering.

To improve the accuracy of gear fault diagnosis and overcome the low diagnostic accuracy of the model caused by manual parameter selection, a combined diagnostic model based on time-frequency fusion features is combined with the improved global search whale optimization algorithm (GSWOA) to optimize the fault diagnosis capability of the kernel extreme learning machine (KELM). First, the time-domain and frequency-domain features of the gear fault state are extracted separately, and feature vectors are constructed through feature fusion, which overcomes the limitations of single features. Second, the GSWOA based on three strategies is used to optimize the regularization coefficient C and kernel function parameter γ of KELM, and a GSWOA-KELM fault diagnosis model is built to avoid the problem of low fault diagnosis accuracy caused by the manual selection of KELM parameters. Finally, the public dataset from Southeast University is taken to verify the performance of the proposed model by comparing it with KELM, SSA-KELM, and WOA-KELM models. The experimental results demonstrate that the improved time-frequency fusion features-based GSWOA-KELM model shows faster convergence speed and stronger global search ability. Compared with KELM, SSA-KELM, and WOA-KELM models, the performance of the proposed model has been improved by 11.33%, 8.67%, and 1.33%, respectively.

This study focuses on the synthesis and evaluation of carbon−coated Ni−Fe alloy catalysts (NiFex@C, x = 0, 0.3, 0.7, 1.1) for the hydrogenation of cinnamaldehyde. The catalysts were characterized using Transmission Electron Microscopy (TEM), X-ray Diffraction (XRD), Raman spectroscopy, and X-ray Photoelectron Spectroscopy (XPS). The introduction of Fe was found to increase the average particle size of the bimetallic catalysts compared to the monometallic Ni@C. Ni existed in both metallic and oxide states, while Fe exhibited multiple oxidation states in the bimetallic catalysts. The carbon layer, approximately 2–3 nm thick, was confirmed to envelop the alloy particles. The catalytic performance of carbon−coated Ni−Fe alloy catalysts indicated that the addition of Fe to Ni@C enhanced the selectivity towards hydrocinnamaldehyde (HCAL), with NiFe0.7@C showing the highest selectivity (~88.6%) but at a reduced conversion rate. The carbon layer played a pivotal role in the stability and reusability of the catalysts. NiFe0.7@C maintained consistent performance over multiple reaction cycles, while NiFe0.7 NPs (without a carbon layer) exhibited significant deactivation. Both catalysts displayed strong magnetism, facilitating easy separation from the reaction mixture. This study sheds light on the significance of the carbon layer in bimetallic catalysts and provides valuable insights for designing efficient catalysts for hydrogenation processes.

Precise regional crop yield estimates based on the high-spatiotemporal-resolution remote sensing data are essential for directing agronomic practices and policies to increase food security. This study used the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), the flexible spatiotemporal data fusion (FSADF), and the spatial and temporal non-local filter based fusion model (STNLFFM) to calculate the normalized differential vegetation index (NDVI) of the summer maize planting area in the Southeast Loess Plateau based on the Sentinel-2 and MODIS data. The spatiotemporal resolution was 10 m and 1 d, respectively. Then, we evaluated the adaptability of the ESTARFM, FSADF, and STNLFFM fusion models in the field from the perspectives of spatial and textural characteristics of the data, summer maize NDVI growing curves, and yield estimation accuracy through qualitative visual discrimination and quantitative statistical analysis. The results showed that the fusion of ESTARFM–NDVI, FSDAF–NDVI, and STNLFFM–NDVI could precisely represent the variation tendency and local mutation information of NDVI during the growth period of summer maize, compared with MODIS–NDVI. The correlation between STNLFFM–NDVI and Sentinel-2–NDVI was favorable, with large correlation coefficients and a small root mean square error (RMSE). In the NDVI growing curve simulation of summer maize, STNLFFM introduced overall weights based on non-local mean filtering, which could significantly improve the poor fusion results at seedling and maturity stages caused by the long gap period of the high-resolution data in ESTARFM. Moreover, the accuracy of yield estimation was as follows (from high to low): STNLFFM (R = 0.742, mean absolute percentage error (MAPE) = 6.22%), ESTARFM (R = 0.703, MAPE = 6.80%), and FSDAF (R = 0.644, MAPE = 10.52%). The FADSF fusion model was affected by the spatial heterogeneity in the semi-humid areas, and the yield simulation accuracy was low. In the semi-arid areas, the FADSF fusion model had the advantages of less input data and a faster response.

The efficient management of aviation safety requires the precise analysis of trends in incidents. While classical statistical models often rely on the autocorrelation of indicator sequences for trend fitting, significant room remains for performance improvement. To enhance the accuracy and interpretability of trend analyses for aviation incidents, we propose the Causal-ARIMA model, which is grounded in causal inference theory, and we employ four distinct modeling strategies to fit the trend of incidents in China’s civil aviation sector between 1994 and 2020. The objective is to validate the performance of the Causal-ARIMA model and identify optimal trend analysis strategies. The four modeling strategies account for causation factors, stationarity, and causality with operational volume, incorporating models like AR, ARMA, ARIMA, and Causal-ARIMA. Our findings reveal that ensemble techniques incorporating the Causal-ARIMA model (Strategy 2 and 3) outperform classical trend analysis methods (Strategy 1) in terms of model fit. Specifically, the causality-based binary fitting technique (Strategy 3) achieves the most uniformly dispersed fitting performance. When the premises for using the Causal-ARIMA model are relaxed, applying it to variables without Granger causal relationships results in uneven model performance (Strategy 4). According to our study, the Causal-ARIMA model can serve as a potent tool for the analysis of trends in the domain of aviation safety. Modeling strategies based on the Causal-ARIMA model provide valuable insights for aviation safety management.

Lack of soil moisture and phosphorus deficiency limits wheat grain yield in dryland areas. However, the moisture-conserving effect of straw mulching combined with phosphor fertilization on fertile florets per spike (FFS) and grain yield remains unclear. During the 2020–2021 and 2021–2022 growing seasons, we investigated the combined effects of straw mulching (0 and 8000 kg ha−1) and phosphorus fertilization (0, 75, and 120 kg P2O5 ha−1) on spike development, assimilates’ availability, and the photosynthetic properties of flag leaves by conducting a field experiment. Compared with no straw mulch control, straw mulching increased fertile spike, grain number per spike (15.6%), and grain yield (22.6%), and grain number per spike was the most important contribution to increasing wheat grain yield (46%). An increase in grain number per spike is associated with FFS. Compared with no straw mulch control, straw mulching increased FFS by 19.5%, and it increased with increasing phosphorus fertilization levels. Moreover, straw mulching combined with phosphorus fertilization promoted the light compensation point (LCP), light saturation point (LSP), net photosynthetic rate (Pn), Chl b, and the maximal photochemical efficiency of photosystem II (Fv/Fm) of flag leaves to produce carbohydrates. Our study has shown that the primary factor for the divergence in FFS under straw mulching and phosphorus application was the efficiency of assimilate utilization in the spike, which ultimately led to increased grain number per spike and grain yield.

Reactive air brazing of porous SiO2 ceramic (p-SiO2) was achieved using Ag-CuO filler metal. When brazing p-SiO2, two main problems existed. Firstly, the wettability of the Ag filler metal on the surface of p-SiO2 was poor. Secondly, the residual stress caused by the mismatch of the coefficient of thermal expansion was high in the joint. In order to solve these problems, the effects of CuO contents on the p-SiO2 brazed joint were analyzed. In a wetting experiment, the addition of CuO significantly improved the wettability of the Ag-CuO/p-SiO2 system. With the content of CuO increasing, the contact angle decreased from 90° to 0°. In addition, when the content of CuO increased to 0.5 mol%, the contact angle decreased from 90° to 52°. Then, during brazing p-SiO2 with the Ag-xCuO filler metal, the typical interfacial microstructure of the joints brazed at 1000 °C for 30 min was p-SiO2 ceramic/Ag (s,s) + SiO2 + CuO/Ag (s,s)/Ag (s,s) + SiO2 + CuO/p-SiO2 ceramic. Meanwhile, Ag-CuO infiltrated into the p-SiO2 ceramic and an infiltration layer formed. The infiltration layer was composed of Ag (s,s) + SiO2 + CuO and the infiltration layer was conductive to form a good gradient transition of the coefficient of thermal expansion (CTE). Then, the residual stress in the joint was released and the shear strength improved. In addition, with the content of CuO increasing, the depth of the infiltration layer increased. Furthermore, when the content of CuO was 0.5 mol%, the maximum shear strength of the joint was 55 MPa.

Two-dimensional (2D) materials with atomic thickness, tunable light-matter interaction, and significant nonlinear susceptibility are emerging as potential candidates for new-generation optoelectronic devices. In this review, we briefly cover the recent research development of typical nonlinear optic (NLO) processes including second harmonic generation (SHG), third harmonic generation (THG), as well as two-photon photoluminescence (2PPL) of 2D materials. Nonlinear light-matter interaction in atomically thin 2D materials is important for both fundamental research and future optoelectronic devices. The NLO performance of 2D materials can be greatly modulated with methods such as carrier injection tuning, strain tuning, artificially stacking, as well as plasmonic resonant enhancement. This review will discuss various nonlinear optical processes and corresponding tuning methods and propose its potential NLO application of 2D materials.

An accurate parameter extraction of the proton exchange membrane fuel cell (PEMFC) is crucial for establishing a reliable cell model, which is also of great significance for subsequent research on the PEMFC. However, because the parameter identification of the PEMFC is a nonlinear optimization problem with multiple variables, peaks, and a strong coupling, it is difficult to solve this problem using traditional numerical methods. Furthermore, because of insufficient current and voltage data measured by the PEMFC, the precision rate of cell parameter extraction is also very low. The study proposes a parameter extraction method using a generalized regression neural network (GRNN) and meta-heuristic algorithms (MhAs). First of all, a GRNN is used to de-noise and predict the data to solve the problems in the field of PEMFC, which include insufficient data and excessive noise data of the measured data. After that, six typical algorithms are used to extract the parameters of the PEMFC under three operating conditions, namely high temperature and low pressure (HTLP), medium temperature and medium pressure (MTMP), and low temperature and high pressure (LTHP). The last results demonstrate that the application of GRNN can prominently decrease the influence of data noise on parameter identification, and after data prediction, it can greatly enhance the precision rate and reliability of MhAs parameter identification, specifically, under HTLP conditions, the V-I fitting accuracy achieved 99.39%, the fitting accuracy was 99.07% on MTMP, and the fitting accuracy was 98.70%.

Biochar is important for soil improvement, fertilizer innovation, and greenhouse gas reduction. In this paper, Mg-modified biochar was prepared from rice and corn straw and mixed with soil at a 1% (w/w) addition in an indoor soil simulation experiment to study the effect of Mg-modified biochar on the chemical properties of acidic soil. The results showed that the addition of Mg-modified biochar reduced soil acidity and improved soil fertility. Compared with the control group, the Mg-modified biochar treatment significantly increased the concentrations of available potassium, available phosphorus, total phosphorus, organic carbon and exchangeable calcium and magnesium in the soil, and effectively increased the concentration of total nitrogen. Rice straw Mg-modified biochar treatment was more effective in increasing the soil-available potassium, available phosphorus, total phosphorus and exchangeable magnesium concentration, while corn straw Mg-modified biochar was more effective in increasing the soil organic carbon and exchangeable calcium concentration. In addition, the high pyrolysis temperature of Mg-modified biochar was more effective in promoting the soil-available potassium, available phosphorus and total nitrogen concentration, while the low pyrolysis temperature of Mg-modified biochar was more effective in promoting soil alkaline nitrogen, exchangeable calcium and magnesium.

Although hundreds of different adjuvants have been tried, aluminum-containing adjuvants are by far the most widely used currently. It is worth mentioning that although aluminum-containing adjuvants have been commonly applied in vaccine production, their acting mechanism remains not completely clear. Thus far, researchers have proposed the following mechanisms: (1) depot effect, (2) phagocytosis, (3) activation of pro-inflammatory signaling pathway NLRP3, (4) host cell DNA release, and other mechanisms of action. Having an overview on recent studies to increase our comprehension on the mechanisms by which aluminum-containing adjuvants adsorb antigens and the effects of adsorption on antigen stability and immune response has become a mainstream research trend. Aluminum-containing adjuvants can enhance immune response through a variety of molecular pathways, but there are still significant challenges in designing effective immune-stimulating vaccine delivery systems with aluminum-containing adjuvants. At present, studies on the acting mechanism of aluminum-containing adjuvants mainly focus on aluminum hydroxide adjuvants. This review will take aluminum phosphate as a representative to discuss the immune stimulation mechanism of aluminum phosphate adjuvants and the differences between aluminum phosphate adjuvants and aluminum hydroxide adjuvants, as well as the research progress on the improvement of aluminum phosphate adjuvants (including the improvement of the adjuvant formula, nano-aluminum phosphate adjuvants and a first-grade composite adjuvant containing aluminum phosphate). Based on such related knowledge, determining optimal formulation to develop effective and safe aluminium-containing adjuvants for different vaccines will become more substantiated.

Soil fungal community characteristics of alpine timberlines are unclear. In this study, soil fungal communities in five vegetation zones across timberlines on the south and north slopes of Sejila Mountain in Tibet, China were investigated. The results show that the alpha diversity of soil fungi was not different between the north- and south-facing timberlines or among the five vegetation zones. Archaeorhizomyces (Ascomycota) was a dominant genus at the south-facing timberline, whereas the ectomycorrhizal genus Russula (Basidiomycota) decreased with decreasing Abies georgei coverage and density at the north-facing timberline. Saprotrophic soil fungi were dominant, but their relative abundance changed little among the vegetation zones at the south timberline, whereas ectomycorrhizal fungi decreased with tree hosts at the north timberline. Soil fungal community characteristics were related to coverage and density, soil pH and ammonium nitrogen at the north timberline, whereas they had no associations with the vegetation and soil factors at the south timberline. In conclusion, timberline and A. georgei presence exerted apparent influences on the soil fungal community structure and function in this study. The findings may enhance our understanding of the distribution of soil fungal communities at the timberlines of Sejila Mountain.

Sulfated glycans from marine organisms are excellent sources of naturally occurring glycosaminoglycan (GAG) mimetics that demonstrate therapeutic activities, such as antiviral/microbial infection, anticoagulant, anticancer, and anti-inflammation activities. Many viruses use the heparan sulfate (HS) GAG on the surface of host cells as co-receptors for attachment and initiating cell entry. Therefore, virion–HS interactions have been targeted to develop broad-spectrum antiviral therapeutics. Here we report the potential anti-monkeypox virus (MPXV) activities of eight defined marine sulfated glycans, three fucosylated chondroitin sulfates, and three sulfated fucans extracted from the sea cucumber species Isostichopus badionotus, Holothuria floridana, and Pentacta pygmaea, and the sea urchin Lytechinus variegatus, as well as two chemically desulfated derivatives. The inhibitions of these marine sulfated glycans on MPXV A29 and A35 protein–heparin interactions were evaluated using surface plasmon resonance (SPR). These results demonstrated that the viral surface proteins of MPXV A29 and A35 bound to heparin, which is a highly sulfated HS, and sulfated glycans from sea cucumbers showed strong inhibition of MPXV A29 and A35 interactions. The study of molecular interactions between viral proteins and host cell GAGs is important in developing therapeutics for the prevention and treatment of MPXV.

Soil, sediment, and waters contaminated with heavy metals pose a serious threat to ecosystem function and human health, and microorganisms are an effective way to address this problem. In this work, sediments containing heavy metals (Cu, Pb, Zn, Mn, Cd, As) were treated differently (sterilized and unsterilized) and bio-enhanced leaching experiments were carried out with the addition of exogenous iron-oxidizing bacteria A. ferrooxidans and sulfur-oxidizing bacteria A. thiooxidans. The leaching of As, Cd, Cu, and Zn was higher in the unsterilized sediment at the beginning 10 days, while heavy metals leached more optimally in the later sterilized sediment. The leaching of Cd from sterilized sediments was favored by A. ferrooxidans compared to A. thiooxidans. Meanwhile, the microbial community structure was analyzed using 16S rRNA gene sequencing, which revealed that 53.4% of the bacteria were Proteobacteria, 26.22% were Bacteroidetes, 5.04% were Firmicutes, 4.67% were Chlamydomonas, and 4.08% were Acidobacteria. DCA analysis indicated that microorganisms abundance (diversity and Chao values) increased with time. Furthermore, network analysis showed that complex networks of interactions existed in the sediments. After adapting to the acidic environmental conditions, the growth of some locally dominant bacteria increased the microbial interactions, allowing more bacteria to participate in the network, making their connections stronger. This evidence points to a disruption in the microbial community structure and its diversity following artificial disturbance, which then develops again over time. These results could contribute to the understanding of the evolution of microbial communities in the ecosystem during the remediation of anthropogenically disturbed heavy metals.

Long landing hazardous events (long landings) are regarded as the most common unsafe events during an aircraft’s landing phase and are significantly influenced by pilots’ leveling operations. This paper proposes a pre-warning method for long aircraft landings based on operation characteristics clustering to better prevent the occurrence of long landing events and develop pre-warning technology for long aircraft landings applicable to actual civil aviation aircraft operations. Based on the quick access recorder (QAR) flight data of a Boeing B737-800 fleet, the Gaussian mixture model (GMM) clustering method was employed to cluster, group, analyze, and evaluate the pilot operation characteristics utilizing the relative indicators of aircraft speed in the takeoff and landing phases as the measurement indices. Moreover, a long landing pre-warning model was developed based on the eXtreme Gradient Boosting (XGBoost) algorithm to account for the overall characteristics of various operations. The complete accuracy, recall ratio, and precision of the long landing pre-warning method based on pilot operation characteristics clustering reached 89.66%, 89.16%, and 92.50%, respectively, in the test of the pre-warning model, demonstrating a significant improvement over those of the pre-warning model without considering the operation characteristics and presenting a more effective pre-warning effect. Optimizing the long landing pre-warning model with pilot operation characteristics can effectively improve the model’s pre-warning capabilities, assist the crew in making accurate decisions, and prevent unsafe events during aircraft landing.

Continuous monitoring of evapotranspiration (ET) at high spatio-temporal resolutions is vital for managing agricultural water resources in arid and semi-arid regions. This study used the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) to calculate the ET of winter wheat between the green-up and milk stages in Linfen Basin, a typical, semi-arid area of the Loess Plateau, at temporal and spatial resolutions of 30 m and 8 d, respectively. We then analyzed the impact of meteorological factors on ET and its variation during the main growth period of winter wheat. The fused ET data displayed the spatial details of the OLI ET data better and could accurately reflect ET variation and local sudden variations during the main growth period of winter wheat. Moreover, winter wheat ET in rain-fed areas is more heavily influenced by meteorological factors, and the effect is more direct. Affected by the synergistic effect of wind velocity, precipitation, and temperature, the ET of winter wheat in rain-fed area was lower in the green-up stage. Then, ET gradually increased, reaching its maximum in the heading–grain filling stage. At the jointing stage, temperature had a significant effect on ET. A combination of precipitation and temperature had the greatest impact on the ET of winter wheat in the heading–filling stage. In the milk stage, meteorological factors had a minor impact on ET. This study serves as a reference for ET in winter wheat in semi-arid areas and its influencing meteorological factors, which can assist in drought mitigation and regional food security strategies.

Designing functional membranes through the collaboration of multi-dimensional nanomaterials is of particular interest in environmental and biomedical applications. Herein, we propose a facile and green synthetic strategy by collaborating with graphene oxide (GO), peptides, and silver nanoparticles (AgNPs) to synthesize functional hybrid membranes with favourable antibacterial effects. GO nanosheets are functionalized with self-assembled peptide nanofibers (PNFs) to form GO/PNFs nanohybrids, in which the PNFs not only improve the biocompatibility and dispersity of GO, but also provide more active sites for growing and anchoring AgNPs. As a result, multifunctional GO/PNFs/AgNP hybrid membranes with adjustable thickness and AgNP density are prepared via the solvent evaporation technique. The structural morphology of the as-prepared membranes is characterized using scanning electron microscopy, transmission electron microscopy, and X-ray photoelectron spectroscopy, and their properties are analyzed by spectral methods. The hybrid membranes are then subjected to antibacterial experiments and their excellent antibacterial performances are demonstrated.

Perovskite solar cells (PSCs), one of the most promising photovoltaic technologies, have been widely studied due to their high power conversion efficiency (PCE), low cost, and solution processability. The architecture of PSCs determines that high PCE and stability are highly dependent on each layer and the related interface, where nonradiative recombination occurs. Conventional synthetic chemical materials as modifiers have disadvantages of being toxic and costly. Natural molecules with advantages of low cost, biocompatibility, and being eco-friendly, and have improved PCE and stability by modifying both functional layers and interface. In this review, we discuss the roles of natural molecules on PSCs devices in terms of the perovskite active layer, interface, carrier transport layers (CTLs), and substrate. Finally, the summary and outlook for the future development of natural molecule-modified PSCs are also addressed.

Osteosarcoma accounts for 28% of primary bone malignancies in adults and up to 56% in children and adolescents (

Chinese fir (Cunninghamia lanceolata) is a kind of evergreen coniferous tree species, the expansion of its pure forest area and multiple generations of continuous planting has led to a decline of stand quality and woodland fertility. To further investigate the relationship between leaf, litter, and soil stoichiometry, microbial community status, and microbial resource limitation of Chinese fir after continuous planting. We studied the C, N, and P stoichiometries of leaf, litter, and soil from successive rotations of Chinese fir plantations. In addition to this, soil microbial biomass C, N, and P, extracellular enzymes, as well as the soil microbial community composition, were determined. The continuous planting of Chinese fir significantly increased the leaf N and P contents and decreased the C content of litter, and the soil C:N and C:P ratios, thus leading to a soil stoichiometric imbalance. The continuous planting of Chinese fir plantations significantly increased the soil microbial biomass. Compared with the first-generation plantations, the N and P contents of the second- and third-generation plantations increased by 37.11%, 21.83% and 46.28%, 73.38%, respectively, thus alleviating the restriction of microbial N and P. Under continuous planting, the extracellular enzyme activities of N (NAG + LAP) and P (AP) were significantly decreased, and those of the second- and third-generation plantations were significantly decreased by 7.05%, 9.43% and 11.79%, 48.94%, respectively, compared with those of the first-generation plantations, resulting in an increase of 7.85 and 3.19% in carbon-use efficiency. The fungi:bacteria (F:B) ratio of the soil microbial community was elevated in successive plantations. The least squares pathway model (PLS-PM) indicated that the stoichiometric ratio of ecological enzymes had an indirect negative effect on CUE, and was the strongest predictor. This study showed that the successive plantation of Chinese fir resulted in a leaf, litter, and soil stoichiometric imbalance, further affecting community composition and resource limitation of soil microorganisms.

Under gas-liquid microextraction (GLME) operating conditions (extraction temperature 270 °C, extraction time 7 min, condensation temperature −2 °C, and carrier nitrogen gas speed 2.5 mL/min), ice cream samples, as a representative food, were pre-treated. The volatile aroma components of each sample was qualitatively analysed using GC-MS. The principal component analysis was conducted to classify the functional groups, which showed that alcohols, acids, esters, ketones, and aldehydes were the main compounds responsible for the aroma of ice cream. It was found that furan-3-carboxaldehyde, 3-furanmethanol, 2(5H)-furanone, 5-methylfuranal, 2,5-diformylfuran, 3-hydroxy-2-methyl-4-pyrone, 5-hydroxymethylfurfural, ethyl maltol, and glycerol were routinely used flavour ingredients in ice cream.

The now prevalent Omicron variant and its subvariants/sub-lineages have led to a significant increase in COVID-19 cases and raised serious concerns about increased risk of infectivity, immune evasion, and reinfection. Heparan sulfate (HS), located on the surface of host cells, plays an important role as a co-receptor for virus–host cell interaction. The ability of heparin and HS to compete for binding of the SARS-CoV-2 spike (S) protein to cell surface HS illustrates the therapeutic potential of agents targeting protein–glycan interactions. In the current study, phylogenetic tree of variants and mutations in S protein receptor-binding domain (RBD) of Omicron BA.2.12.1, BA.4 and BA.5 were described. The binding affinity of Omicron S protein RBD to heparin was further investigated by surface plasmon resonance (SPR). Solution competition studies on the inhibitory activity of heparin oligosaccharides and desulfated heparins at different sites on S protein RBD–heparin interactions revealed that different sub-lineages tend to bind heparin with different chain lengths and sulfation patterns. Furthermore, blind docking experiments showed the contribution of basic amino acid residues in RBD and sulfo groups and carboxyl groups on heparin to the interaction. Finally, pentosan polysulfate and mucopolysaccharide polysulfate were evaluated for inhibition on the interaction of heparin and S protein RBD of Omicron BA.2.12.1, BA.4/BA.5, and both showed much stronger inhibition than heparin.

Hydrogen peroxide (H2O2) is one of the intermediates or final products of biological metabolism and participates in many important biological processes of life activities. The detection of H2O2 is of great significance in clinical disease monitoring, environmental protection, and bioanalysis. In this study, Ti3C2-based nanohybrids are prepared by the biological modification and self-assembled peptide nanosheets (PNSs)-based biomimetic synthesis of copper sulfide nanoparticles (CuS NPs), which show potential application in the fabrication of low-cost and high-performance electrochemical H2O2 biosensors. The synthesized CuS-PNSs/Ti3C2 nanohybrids exhibit excellent electrochemical performance towards H2O2, in which CuS NPs can catalyze the decomposition of H2O2 and realize the transformation from a chemical signal to an electrical signal to achieve the purpose of H2O2 detection. The prepared CuS-PNSs/Ti3C2-based electrochemical biosensor platform exhibits a wide detection range (5 μM–15 mM) and a low detection limit (0.226 μM). In addition, it reveals good selectivity and stability and can realize the monitoring of H2O2 in a complex environment. The successful biomimetic synthesis of CuS-PNSs/Ti3C2 hybrid nanomaterials provides a green and friendly strategy for the design and synthesis of functional nanomaterials and also provides a new inspiration for the construction of highly effective electrochemical biosensors for practical detection of H2O2 in various environments.

In the process of diagenesis and burial of sedimentary basins, basin fluid activities participate in the process of hydrocarbon accumulation and metal mineralization. Understanding the evolution of basin fluid is of great significance in revealing the related hydrocarbon accumulation and mineralization. Paleo-reservoirs are closely associated with Carlin-type gold deposits in the Nanpanjiang-Youjiang Basin, South China. Calcite, the fluid activity product, is closely related to bitumen and gold-bearing pyrite. By integrating petrographic, cathode luminescence, and fluid inclusion analysis, as well as the relevant chronological results of predecessors, this paper attempts to establish the relationship between fluid evolution, hydrocarbon accumulation, and gold mineralization. Two types of calcite (black/gray and white) developed in the Banqi-Yata-Laizishan area, the Nanpanjiang-Youjiang Basin. Black/gray calcite is symbiotic with bitumen and features dark red colors in cathode luminescence. Many hydrocarbon inclusions developed along with fluid inclusion analysis at low homogenization temperatures (65.7~173.1 °C). Combining the previously reported U-Pb ages (~250–230 Ma) of this kind of calcite with some geochemistry data on the associated reservoir and gold deposit, this calcite records the consecutive hydrocarbon accumulation and Carlin-type gold mineralization from the Late Permian to the Late Triassic periods controlled by Indosinian tectonic movement. The white calcite featuring bright red in cathodoluminescence is symbiotic with gold-bearing pyrite and realgar, and the associated fluid inclusions have high homogenization temperatures (128.2~299.9 °C). Combined with regional tectonic background and isotopic chronology (~140–106 Ma), it seems to record the early Cretaceous Carlin-type gold mineralization controlled by the subduction of the paleo-Pacific plate in the late Yanshanian period.

Shrub species have increased in density and cover in desertification areas, however, the role of sandy habitats in contributing to the expansion of shrubs is poorly understood. Although the effect of sandy habitats on plant growth and reproduction have been demonstrated, most existing studies lack either experimental demonstration or an integrated study during the whole shrub life cycle. We performed field and laboratory experiments to examine the responses of four stages in the life cycle of shrubs (seed germination, plant growth, seed reproduction, clonal reproduction) to sandy habitats (including sand substrate, sand burial and wind erosion) for Caragana shrubs. Results showed that both sand substrate and sand burial facilitated seed germination, seedling biomass, sapling establishment, plant growth, and root-shoot ratio of Caragana. Meanwhile, they both strongly increased seed number and seed preservation, and thus enhanced sexual reproduction. Sand burial favored clonal reproduction of Caragana by promoting the formation of branch-derived ramets, while wind erosion benefited clonal reproduction by facilitating the formation of root-derived ramets. These results suggested that sandy habitats facilitated seed germination, plant growth, sexual reproduction, and clonal reproduction of Caragana, which could explain why shrub abundance, shrub area and shrub height of Caragana in sandy areas was higher than in grasslands. Our study provided an experimental demonstration that sandy habitats promoted the population growth of Caragana shrubs during the whole life cycle and highlighted the significant role of sandy habitats in facilitating shrub encroachment in grasslands.

A high-quality joint of pressureless sintered SiC ceramic (PS-SiC) and stainless steel brazed by AgCu/AgCuTi brazing alloy was realized. The effects of brazing temperature and holding time on the interfacial microstructure and mechanical properties of PS-SiC-stainless steel were analyzed. During brazing, Fe and Cr diffused into a brazing seam, and the typical interfacial microstructure of joints brazed at 890 °C for 15 min was stainless steel/Cr5Si3 + FeSi2 + Ag(s,s)/Cr5Si3 + FeSi2 + Ag(s,s) + Cu(s,s) + TiSi2 + TiC/FeSi2 + Ag(s,s) + Cu(s,s) + TiSi2/Ag(s,s) + Cu(s,s) + SiC/PS-SiC ceramic. Cu-Fe system improved the toughness of the joint, and the shear strength of the joint increased to ~67 MPa. With increasing brazing temperature and holding time, the element Fe continued diffusing into the brazing seam, and the uniformity of Cu-Fe system became worse. Then Cu(s,s) infiltrated into the surface layer of PS-SiC ceramic and FeSi2 brittle phase gathered in the brazing seam. Then, the brittleness of the joint increased and the shear strength of the joint decreased.

In the fiberglass industry, Pt–Rh bushings made of platinum and rhodium have very good characteristics, such as high temperature resistance, corrosion resistance, oxidation resistance, and creep resistance. In this paper, a semi-infinite lath structure model is constructed, and the expression of the surface temperature distribution of a Pt–Rh alloy plate with a circular through hole is obtained based on the non-Fourier heat conduction equation, complex function method and conformal mapping method. At the same time, the influence of the position of the circular through hole in the Pt–Rh bushing and the parameters of the incident light source (Non-diffusion incident wave number and relative thermal diffusion length) on the surface temperature distribution of the Pt–Rh bushing is studied by using this formula. It is found that: 1. heat concentration and fracture are occur easily at the through hole; 2. when the through hole is in the asymmetric center, the greater the asymmetry, the smaller the maximum temperature amplitude; 3. when the buried depth of the through hole increases, the maximum temperature amplitude decreases; 4. when the incident wave number and the relative thermal diffusion length of the incident light source are larger, the maximum temperature amplitude is smaller. The numerical results are almost consistent with those of ANSYS thermal simulation. The expression of the surface temperature distribution of the semi-infinite lath structure proposed in this paper can effectively reduce the loss of precious metal materials and the time of thermal simulation in the experimental process, as well as provide important significance for structural design, quality inspection, process optimization, and service life improvement of Pt–Rh bushings.

CrFeCoNi and CrMnFeCoNi coatings were prepared by laser remelting method. The grain boundary characteristics distribution of both coatings was investigated by electron backscattered diffraction technique. The results showed that the CrFeCoNi coating exhibited higher fraction of low angle grain boundaries and Σ3 boundaries compared to the CrMnFeCoNi coating. The corrosion properties of the CrFeCoNi and CrMnFeCoNi coatings in 3.5 wt% NaCl solution were identified by electrochemical method and localized electrochemical impedance spectroscopy technique. The results indicated that the CrFeCoNi coating had a higher corrosion resistance than the CrMnFeCoNi coating. The better corrosion resistance of the CrFeCoNi coating could be attributed to the high fraction of Σ3 boundaries and low fraction of the high angle boundaries.

Cholesterol, an important lipid in animal membranes, binds to hydrophobic pockets within many soluble proteins, transport proteins and membrane bound proteins. The study of cholesterol–protein interactions in aqueous solutions is complicated by cholesterol’s low solubility and often requires organic co-solvents or surfactant additives. We report the synthesis of a biotinylated cholesterol and immobilization of this derivative on a streptavidin chip. Surface plasmon resonance (SPR) was then used to measure the kinetics of cholesterol interaction with cholesterol-binding proteins, hedgehog protein and tyrosine phosphatase 1B.

The disturbance in gut microbiota composition and metabolism has been implicated in the process of pathogenic bacteria infection. However, the characteristics of the microbiota and the metabolic interaction of commensals–host during pathogen invasion remain more than vague. In this study, the potential associations of gut microbes with disturbed lipid metabolism in mice upon carbapenem-resistant Escherichia coli (CRE) infection were explored by the biochemical and multi-omics approaches including metagenomics, metabolomics and lipidomics, and then the key metabolites–reaction–enzyme–gene interaction network was constructed. Results showed that intestinal Erysipelotrichaceae family was strongly associated with the hepatic total cholesterol and HDL-cholesterol, as well as a few sera and fecal metabolites involved in lipid metabolism such as 24, 25-dihydrolanosterol. A high-coverage lipidomic analysis further demonstrated that a total of 529 lipid molecules was significantly enriched and 520 were depleted in the liver of mice infected with CRE. Among them, 35 lipid species showed high correlations (|r| > 0.8 and p < 0.05) with the Erysipelotrichaceae family, including phosphatidylglycerol (42:2), phosphatidylglycerol (42:3), phosphatidylglycerol (38:5), phosphatidylcholine (42:4), ceramide (d17:1/16:0), ceramide (d18:1/16:0) and diacylglycerol (20:2), with correlation coefficients higher than 0.9. In conclusion, the systematic multi-omics study improved the understanding of the complicated connection between the microbiota and the host during pathogen invasion, which thereby is expected to lead to the future discovery and establishment of novel control strategies for CRE infection.

Monkeypox virus (MPXV), a member of the Orthopoxvirus genus, has begun to spread into many countries worldwide. While the prevalence of monkeypox in Central and Western Africa is well-known, the recent rise in the number of cases spread through intimate personal contact, particularly in the United States, poses a grave international threat. Previous studies have shown that cell-surface heparan sulfate (HS) is important for vaccinia virus (VACV) infection, particularly the binding of VACV A27, which appears to mediate the binding of virus to cellular HS. Some other glycosaminoglycans (GAGs) also bind to proteins on Orthopoxviruses. In this study, by using surface plasmon resonance, we demonstrated that MPXV A29 protein (a homolog of VACV A27) binds to GAGs including heparin and chondroitin sulfate/dermatan sulfate. The negative charges on GAGs are important for GAG–MPXV A29 interaction. GAG analogs, pentosan polysulfate and mucopolysaccharide polysulfate, show strong inhibition of MPXV A29–heparin interaction. A detailed understanding on the molecular interactions involved in this disease should accelerate the development of therapeutics and drugs for the treatment of MPXV.

Carbon material-based aerogels (CMBAs) have three-dimensional porous structure, high specific surface area, low density, high thermal stability, good electric conductivity, and abundant surface-active sites, and, therefore, have shown great application potential in energy storage, environmental remediation, electrochemical catalysis, biomedicine, analytical science, electronic devices, and others. In this work, we present recent progress on the fabrication, structural design, functional tailoring, and gas adsorption applications of CMBAs, which are prepared by precursor materials, such as polymer-derived carbon, carbon nanotubes, carbon nanofibers, graphene, graphene-like carbides, fullerenes, and carbon dots. To achieve this aim, first we introduce the fabrication methods of various aerogels, and, then, discuss the strategies for regulating the structures of CMBAs by adjusting the porosity and periodicity. In addition, the hybridization of CMBAs with other nanomaterials for enhanced properties and functions is demonstrated and discussed through presenting the synthesis processes of various CMBAs. After that, the adsorption performances and mechanisms of functional CMBAs towards CO2, CO, H2S, H2, and organic gases are analyzed in detail. Finally, we provide our own viewpoints on the possible development directions and prospects of this promising research topic. We believe this work is valuable for readers to understand the synthesis methods and functional tailoring of CMBAs, and, meanwhile, to promote the applications of CMBAs in environmental analysis and safety monitoring of harmful gases.

Dam deformation is an intuitive and reliable monitoring indicator for dam structural response. With the increase in the service life of the project, the structural response and environmental quantity data collected by the structural health monitoring (SHM) system show a geometric growth trend. The traditional hydraulic-seasonal-time (HST) model shows poor performance in dealing with massive monitoring data due to the multidimensional data collinearity problem and the inaccurate temperature field simulations. To address these problems, this study proposes a data-driven dam deformation monitoring model for dealing with massive monitoring data based on the light gradient boosting tree (LGB) and Bayesian optimization (BO) algorithm. The proposed BO–LGB method can mine the underlying relationship between temperature changes and dam deformation instead of simple harmonic functions. Moreover, LGB is used to simulate the relationship between high-dimensional environmental quantity data and dam displacement changes, and the BO algorithm is used to determine the optimal hyperparameter selection of LGB based on massive monitoring data. A concrete dam in long-term service was used as the case study, and three typical dam displacement monitoring points were used for model training and validation. The experimental results have indicated that the method can properly consider the collinearity in variables, and has a good balance in modeling accuracy and efficiency when dealing with high-dimensional large-scale dam monitoring data. Moreover, the proposed method can explain the contribution difference between different input variables to select the factors with a more significant influence on modeling.

Obtaining surface albedo data with high spatial and temporal resolution is essential for measuring the factors, effects, and change mechanisms of regional land-atmosphere interactions in deserts. In order to obtain surface albedo data with higher accuracy and better applicability in deserts, we used MODIS and OLI as data sources, and calculated the daily surface albedo data, with a spatial resolution of 30 m, of Guaizi Lake at the northern edge of the Badain Jaran Desert in 2016, using the Spatial and Temporal Non-Local Filter-based Fusion Model (STNLFFM) and topographical correction model (C model). We then compared the results of STNLFFM and C + STNLFFM for fusion accuracy, and for spatial and temporal distribution differences in surface albedo over different underlying surfaces. The results indicated that, compared with STNLFFM surface albedo and MODIS surface albedo, the relative error of C + STNLFFM surface albedo decreased by 2.34% and 3.57%, respectively. C + STNLFFM can improve poor applicability of MODIS in winter, and better responds to the changes in the measured value over a short time range. After the correction of the C model, the spatial difference in surface albedo over different underlying surfaces was enhanced, and the spatial differences in surface albedo between shifting dunes and semi-shifting dunes, fixed dunes and saline-alkali land, and the Gobi and saline-alkali land were significant. C + STNLFFM maintained the spatial and temporal distribution characteristics of STNLFFM surface albedo, but the increase in regional aerosol concentration and thickness caused by frequent dust storms weakened the spatial difference in surface albedo over different underlying surfaces in March, which led to the overcorrection of the C model.

In this study, SiCp/Al composites were bonded using the laser-induced exothermic bonding method. The nanostructured Al/Ni energetic materials were prepared by the high-energy ball-milling method and served as the bonding interlayer. The joint microstructure was characterized by SEM, EDS, TEM, and XRD. The effect of Zr content on the joint microstructure and shear strength was investigated. The results indicated that after the ball-milling process the Al and Ni particles underwent strong plastic deformations and were welded to each other, forming the nanostructured Al/Ni energetic materials with a lamellar structure. Compared with the raw powders, the location of the exothermic peak decreased by 42 K, and its exothermic performance was significantly improved. The exothermic reactions that occurred in the Al/Ni interlayer provided the required heat for the bonding process. Near the bonding interface, the interlayer could not react completely due to the cooling effect of the substrates, forming a mixture of residual metal particles and Ni-Al compounds. The addition of Zr content enhanced the interfacial reactions between the bonding interlayer and the SiCp/Al composites. The interlayer products transformed from NiAl to the eutectic organization of NiAl + Ni-Al-Zr, thus decreasing the pores in the joint and improving the bonding quality. With an increase in the Zr content, the joint shear strength first increased and then decreased. When the Zr content was 10 wt.%, the joint shear strength reached a maximum of 22 MPa.

Cotton is a major fiber crop in the world that can be severely infested by pests in agricultural fields. Identifying new insect-resistance genes and increasing the expression of known insect-resistance genes are imperative in cultivated cotton. Galanthus nivalis agglutinin (GNA), a lectin that is toxic to both chewing and sucking pests, is mainly expressed in monocotyledons. It is necessary to improve the expression of the GNA protein and to test whether the lectin confers insect resistance to dicotyledons plants. We report a modified GNA gene (ASGNA) via codon optimization, its insertion into Arabidopsis thaliana, and transient expression in cotton to test its efficacy as an insect-resistance gene against cotton aphids and Plutella xylostella. The amount of ASGNA in transgenic plants reached approximately 6.5 μg/g of fresh weight. A feeding bioassay showed that the survival rate of aphids feeding on the leaves of ASGNA transgenic plants was lower than those of aphids feeding on the leaves of non-optimized GNA (NOGNA) transgenic plants and wild-type plants. Meanwhile, the fertility rate was 36% when fed on the ASGNA transgenic plants, while the fertility was 70% and 95% in NOGNA transgenic plants and wild-type plants. Correspondingly, the highest mortality of 55% was found in ASGNA transgenic lines, while only 35% and 20% mortality was observed in NOGNA transgenic plants and wild-type plants, respectively. Similar results were recorded for aphids feeding on cotton cotyledons with transient expression of ASGNA. Taken together, the results show that ASGNA exhibited high insecticidal activity towards sap-sucking insects and thus is a promising candidate gene for improving insect resistance in cotton and other dicotyledonous plants.

Graphene-based nanomaterials have shown wide applications in antimicrobial fields due to their accelerated rate of pathogen resistance and good antimicrobial properties. To apply graphene materials in the antimicrobial test, the graphene materials are usually fabricated as two-dimensional (2D) membranes. In addition, to improve the antimicrobial efficiency, graphene membranes are modified with various functional nanomaterials, such as nanoparticles, biomolecules, polymers, etc. In this review, we present recent advances in the fabrication, functional tailoring, and antimicrobial applications of graphene-based membranes. To implement this goal, we first introduce the synthesis of graphene materials and then the fabrication of 2D graphene-based membranes with potential techniques such as chemical vapor deposition, vacuum filtration, spin-coating, casting, and layer-by-layer self-assembly. Then, we present the functional tailoring of graphene membranes by adding metal and metal oxide nanoparticles, polymers, biopolymers, metal–organic frameworks, etc., with graphene. Finally, we focus on the antimicrobial mechanisms of graphene membranes, and demonstrate typical studies on the use of graphene membranes for antibacterial, antiviral, and antifungal applications. It is expected that this work will help readers to understand the antimicrobial mechanism of various graphene-based membranes and, further, to inspire the design and fabrication of functional graphene membranes/films for biomedical applications.

Widespread hexavalent chromium (Cr (VI)) in the environment has become a worldwide problem, and economical and efficient treatment is urgently needed. In this paper, the treatment method of Cr (VI) by microorganisms and iron minerals (pyrite and magnetite) under anaerobic conditions was investigated. Furthermore, the influence of Cr (VI) on the microbial community structure was explored. The reduction test demonstrated that the removal rate of Cr (VI) in a single biological group was 54.96%; however, in the pyrite and biological groups and magnetite and biological groups, the removal rates of Cr (VI) increased to 83.06% and 78.23%, respectively. Microorganisms and iron minerals work together to produce a better removal effect on the removal rate of Cr (VI). Mechanistic studies have found that in the process of Cr (VI) reduction, a passivation layer is formed on the surface of the mineral that hinders the progress of the reaction. The addition of bacteria can reduce the negative impact of the passivation layer. At the same time, iron minerals have better electron-receiving and -conducting ability and can be used as electron carriers for bacteria to reduce Cr (VI). In addition, iron minerals and the disappearance of Cr (VI) will change the structure of the community and affect the expression of its functions, which is more conducive to reducing Cr (VI). This work sheds new light on the treatment of heavy metal pollution and the understanding of the synergistic reduction mechanism of Cr (VI).