Mangroves are unique ecosystems that thrive in coastal areas,displaying a blend of terrestrial and marine characteristics. They hold significant ecological value and provide essential ecological services. In order to explore the current state of research in field of mangrove studies,this article utilizes the Web of Science core collection database spanning from 1996 to 2022. By employing visualization literature analysis software called Cite Space,statistical analysis was conducted on various aspects such as publication volume and trends,disciplines and journals,primary research countries and institutions,as well as research frontiers and hotspots. This comprehensive analysis aimed to offer insights into the latest developments and areas of focus within mangrove research. The results showed that:1) Since 1996,the number of publications in field of mangrove research has been continuously increasing,especially at the cut-off point of 2008,and the number of publications has shown an exponential growth trend since then; 2) The main disciplines involved include marine and freshwater biology,environmental science,and ecology; 3) The influential journals include Science,Nature,and Science of the Total Environment; 4) The main countries that publish articles are China,the United States,India,Brazil,and Australia,with China ranking first in terms of publication volume; 5) The main sending agencies are the Chinese Academy of Sciences,Florida State University,Sun Yat-sen University and Xiamen University; 6) The protection,management,and restoration of mangrove ecosystems,climate change,biodiversity,sediments,and blue carbon are all current research hotspots in the field of mangroves. Remote sensing data analysis,mangrove diversity and function,carbon storage,coral reef fish,and endophytic fungi such as Aspergillus are research frontiers in the field of mangrove research. This article reveals the current status,hotspots,and future development trends of mangrove research,providing reference for subsequent related research in this field.

The explosive growth of information provides a realistic foundation for the study of multi-source data fusion,continuously expanding its scope of data incorporation and application prospects. The advancement of artificial intelligence technologies further offers innovative possibilities. To sort out the historical context,current status,and frontier trends of multi-source data fusion research,this paper utilizes CiteSpace software to conduct a visual analysis of relevant studies in the CNKI and Web of Science (WOS) databases,focusing on publication volume by year,institutional co-occurrence,author co-occurrence,keyword co-occurrence,keyword clustering,and prominent words from 1992 to 2022. The results indicate that in recent years,research on this topic,both domestically and internationally,has progressively matured,with unification of concepts and expansion of integrated methods in interdisciplinary fields,entering a period of significant development. Chinese research institutions and author networks are relatively loose,with focused hotspots on data fusion-centric information fusion,multi-source heterogeneous data,etc.,characterized by emphasis on cross-sectoral integration,algorithm optimization,and cross-disciplinary applications. In contrast,foreign research institutions and author networks are more mature and stable,with a broader range of hotspots including multi-source information fusion,lidar data,etc.,characterized by emphasis on heterogeneous integration and deep insights. In the future,related research will evolve alongside the development of artificial intelligence technology,delving into more diverse advanced algorithm designs and specific scenario applications. The research findings can assist researchers in topic selection and frontier identification,contributing to the improvement of research quality and innovative development.

Furfural as an important biomass-derived platform molecule can be selectively hydrogenated over aldehyde group into other chemicals under the assistance of catalysts. Metal-organic framework (MOF) is a kind of crystalline porous material with periodic networks assembled by metal centers/clusters and ligands,which is often used as carrier and sources or directly used as catalyst for furfural hydrogenation with high performance. This paper summarizes design principle of MOF-based catalysts and research progress in selective furfural hydrogenation,and the critical factors and mechanisms for the reactions are analyzed. Challenges for MOF-based catalysts in hydrogenation are also analyzed and the effective acidic site regulation is a key issue,providing a reference for future synthesis of MOF-based catalysts and their furfural hydrogenation applications.

Accurate prediction of heating load not only effectively reduces energy consumption but also improves the efficiency of the heating system and user comfort. To enhance the accuracy of heating load prediction, this study combines the Variational Mode Decomposition (VMD) algorithm with an improved Informer model for heating load prediction. Firstly, the VMD algorithm is used to decompose the heating load data, reducing its non-stationarity. Secondly, relative positional encoding is introduced in the Informer model to better capture the dependencies in the sequential data and avoid information leakage, replacing the absolute positional encoding. Furthermore, dilated causal convolution is adopted instead of regular convolution to increase the receptive field and enhance the extraction of local information. Comparative experiments with mainstream prediction models (GRU, LSTM, Transformer, and Informer) are conducted on multiple datasets. The experimental results demonstrate that the RDC-Informer model achieves an R² evaluation metric of 98.3%, which is 11.6%, 6.3%, 4.7%, and 2.6% higher than the comparative models, respectively. Additionally, the effectiveness of the dilated causal convolution is verified by increasing the convolution kernel, confirming the applicability and accuracy of the RDC-Informer model and providing a reference for further improvement in real-time smart heating.

A differential passive N-path filter based on switching capacitors is designed to address the issue that traditional N-path filters are unable to suppress harmonics and harmonic aliasing,and have certain insertion losses. This filter eliminates odd harmonics by using capacitance weighted multiphase signals with different capacitance values,suppresses harmonic aliasing by combining different clock signals,and uses capacitance stacking technology to provide certain gain and reduce insertion loss. The filter adopts TSMC 40 nm CMOS process,and the simulation results show that the center frequency fs of the filter can be adjusted from 0.3-1.5 GHz,and the rejection effect of f_s=500 MHz is more than 48 dB for the 3rd and 5th harmonics,and the suppression of the most powerful harmonic folding reaches more than 60 dB,and there is no insertion loss,the noise figure (NF) is 2.5-2.8 dB,and the linearity (IIP3) is above 15 dBm.

LLC resonant converter has some limitations in soft switching characteristics. Aiming at the problem of unstable soft switching characteristics of LLC resonant converter under variable load conditions,the soft switching constraints are derived based on ZVS/ZCS boundary. A set of parameter optimization design method based on full-bridge LLC resonant converter under variable load is proposed to improve the stability and efficiency of the system. The scheme not only considers the parameter optimization of resonant inductor,capacitor and transformer,but also fully considers the influence of load change on soft switching behavior. The working principle of the full-bridge LLC resonant converter is introduced,and the voltage gain and the impedance characteristics of the resonant network are analyzed in detail. According to the above design scheme,a 500 W vehicle charger test prototype is built and tested under laboratory conditions. The results show that the maximum efficiency of the test prototype can reach 96.36%.

Permanent magnet synchronous motor (PMSM) is a multivariable and highly coupled nonlinear system,which is subject to chaotic oscillations during practical operation. In order to suppress the chaos of the PMSM system,a fixed-time bounded controller without initial state constraints is designed,which can make the system reach a stable state within 2.8 s. Firstly,the virtual controller is designed according to the virtual errors of each subsystem of the PMSM system,and the controller is derived by backstepping to have high anti-interference performance and robustness. Secondly, it is proved that the time-varying feedback parameter ensures that the system achieves asymptotic stabilization while arriving at the finite-time convergence,and the stabilization time is only related to the prescribed boundary. Finally,numerical simulations are carried out by adjusting the control coefficients k so that the system can reach stability quickly under different initial states,and it is verified that the designed controller is able to make the system reach asymptotic stability and the output converge to the given boundary regardless of the initial state of the system.

Accurate and fast detection of fruits in orchards is one of the key tasks for intelligent agricultural approaches,such as fruit yield prediction and automated harvesting. A lightweight detection method based on an improved YOLOv7-Tiny is proposed in this paper to address the current issue of large parameters and FLOPs in object detection models. The method is specifically designed for detecting passion fruit in complex orchard environments,aiming to enhance real-time applicability on embedded devices. Firstly,the Omni-dimensional Dynamic Convolution (ODConv) is employed in the backbone network to enhance its feature extraction capability,thereby increasing the mean Average Precision (mAP) by 2 percentage points. Furthermore,to reduce the parameters and FLOPs of the neck network,the GMConv lightweight module is proposed by integrating the GhostNet network and the MobileOne network. The parameters and FLOPs have decreased by approximately 30% and 20%,respectively,and the model's FPS has increased by around 50 frame/s. Experimental results on the passion fruit dataset reveal that,compared with YOLOv7-Tiny,the parameters and FLOPs of the improved algorithm have decreased by 32.1% and 25.4% respectively,while the mAP has increased by 2.6 percentage points. With the reduction of FLOPs and parameters,the improved algorithm further enhances detection accuracy,offering theoretical research and technical support for deployment on embedded devices.

The hybrid recommendation model,named IEU-DeepCFM (deep and cross factorization machine information extraction unit),is proposed in this paper,which is based on the deep factorization machine and integrates the information extraction unit and cross network structure. In the proposed model,a fixed representation of each feature is learned by most existing recommendation methods. However,it is recognized that user behavioral preferences change with contextual features,and features have different importance in different contexts. Therefore,inaccurate recommendation results may be caused by the fixed representation of features given by the model. To address this issue,the information extraction unit module is introduced,consisting of a self-attention mechanism and a contextual information extractor. This module learns context-aware feature representations for each feature in various contexts. Subsequently,a deep cross factorization machine is employed to mine low- and high-order features of the user. This enables users to receive more explicit cross-information,ultimately leading to click-through rate predictions based on user behavioral characteristics. The results of ablation and comparison experiments conducted on the MovieLens movie dataset and the Avazu advertising click-through rate dataset demonstrate the improvement in both AUC and LogLoss indicators achieved by the proposed model. This confirms the rationality of the model.

To address the nonsmooth pseudoconvex optimization problems encountered in practical applications,an innovative solution is proposed:a single-layer neural network algorithm that integrates the concept of penalty functions and the theory of differential inclusions. Firstly,through mathematical theory,it is proved that this algorithm can ensure that the state solutions ultimately converge to the optimal solution of the pseudoconvex optimization problem,thus establishing the correctness of the proposed algorithm. Secondly,the effectiveness of the algorithm is further verified through the analysis of simulated convergence results from two numerical experiments. Finally,the applications of this algorithm to practical problems demonstrate its practical application value in solving pseudoconvex optimization issues. Compared with existing neural network algorithms,this algorithm can not only solve more general pseudoconvex optimization problems with convex inequality and equality constraints but also tackle practical application issues. Moreover,the algorithm has a simple hierarchical structure,does not require the calculation of precise penalty parameters,allows for the selection of any starting point,and does not add any auxiliary variable, which thus provides an effective approach to solving pseudoconvex optimization problems.

This paper studies an infectious disease model we obtain with media coverage, vaccination and isolation. By using the basic reproduction number R₀ and Lyapunov function, it is shown that the disease free equilibrium is global asymptotically stable if R₀ ≤1, which implies that the disease will disappear, and that the endemic equilibrium is global asymptotically stable if R₀>1, which implies that the disease will persist. Finally, the conclusion is verified by numerical analysis.

The partial linear variable coefficient model consists of two parts,parameter and non-parameter,which has the advantages of wide range of adaptation and strong interpretation. To solve the parameter estimation problem of the model,the B-spline method is used to approximate the unknown smooth function of the non-parametric part,and then the compound asymmetric Laplacian distribution is used to realize the Bayesian composite quantile regression,and the posterior distribution of all the unknown parameters is derived based on the Gibbs sampling algorithm. Through numerical simulation,Bayesian compound quantile regression is compared with Bayesian quantile regression and Bayesian linear regression parameter estimation. The results show that when the error follows non-normal distribution,Bayesian compound quantile regression estimation performs better under mean square error criterion. Finally,based on the above three methods to predict the case data,the results show that in terms of mean absolute deviation and mean square error prediction,the prediction effect based on Bayesian compound quantile regression is the best.

In this paper,the Bayesian empirical likelihood method is extended to the compound quantile regression model. Firstly,the empirical likelihood function of the compound quantile regression model is constructed, and the conditional posterior distribution of unknown parameters is derived after the prior information is given. Secondly, considering that the posterior distribution of unknown parameters is complex and has implicit equation constraints,a Metropolis-Hastings algorithm with constraints is constructed for point estimation,confidence interval estimation and parameter hypothesis testing of model parameters. The computer simulation results show that when the stochastic error of the model is a thick tail distribution,the Bayesian empirical likelihood compound quantile regression method proposed in this paper has more obvious advantages than the compound quantile regression method,the quantile regression method and the least square method in estimating deviation and variance. Especially when the data contains more anomalies,the proposed method is the most robust. Finally,the paper uses the new method to model and analyze the data of a medical expenditure influencing factor,and finds that compared with other estimation methods,the coefficient obtained by Bayes empirical likelihood compound quantile regression method changes the least before and after estimation,regardless of whether the abnormal points in the data are deleted or not. This provides useful assistance in reducing the impact of unknown outtiers in the date on the model during a real modeling process.

Based on topological principles and density functional theory,the catalytic reaction mechanism and the reasons behind the activity of the Co₂Mo₂P₃ cluster were investigated. Using the Gaussian09 program at the B3LYP/Lanl2dz level,full parameter optimization and computational analysis of the cluster's initial configuration were performed. Through an analysis of the contributions of each atom in the Co₂Mo₂P₃ cluster to the HOMO and LUMO orbitals,we found that the Co atoms contribute 53.511% and 59.013% to the frontier orbitals,indicating their potential as active sites in the cluster. Additionally,observations of the density of states and HOMO-LUMO plots revealed that the Co atoms played a significant role in generating the peaks in the Fermi energy levels. Further analysis of the energy gap and Koopmans' theorem showed that configurations 1⁽⁴⁾ and 3⁽⁴⁾ had strong electron-gaining and electron-donating capabilities,respectively,and possess higher catalytic activity compared with other configurations. These results provide robust support for a deeper understanding of the catalytic performance of the Co₂Mo₂P₃ cluster. The findings of this study offer essential theoretical insights into the catalytic reaction mechanism and activity generation of the Co₂Mo₂P₃ cluster,providing valuable references for further optimizing catalytic performance and designing efficient catalysts.

In order to solve the pollution of lead (Pb) in the environment and to properly dispose of red mud,an industrial solid waste generated in alumina oxide smelting. In this study,red mud was used as raw material,sodium carboxymethyl cellulose (CMC) and acrylic acid (AA) were introduced through polymerization and cross-linking reaction to prepare red mud-polyacrylic acid-carboxymethyl cellulose (RMAAC) composite hydrogel. It had adsorption performance,strong stability and high recycling rate,which were used as adsorbents for adsorption of Pb²⁺ in water. The adsorption of Pb²⁺ by RMAAC was mainly attributed to monomolecular layer chemisorption with the involvement of physisorption. The adsorption mechanisms may include ion exchange,functional group complexation,cation-π and ligand chelation. The maximum adsorption amount calculated by Langmuir model was 730.16 mg/g,and excellent adsorption properties within a broad pH range of 3 to 6. RMAAC showed high selective adsorption capacity for Pb²⁺ in the pollution of multiple heavy metal ions.

Common Cnidium and Sophora flavescens are commonly used antipruritic herbs,but their combined effect on itch has not been fully elucidated. In this study,ICR mice were gavaged with Shechuang-Kushen Decoction,and the effect of the drug on the pruritus behavior of the mice was observed by a behavioral method. The changes in vascular permeability were observed by Evans Blue staining. The effect of the decoction on the release of cytokines from the mast cell line RBL-2H3 was evaluated by ELISA and Western Blotting,respectively. The results showed that the decoction inhibited pruritus behavior and increased vascular permeability,but did not affect the exploration ability or muscle tone of the mice. The decoction solution reduced the release of β-hexosaminidase,histamine,IL-31,IL-4,IL-13,and CXCL10,and inhibited the phosphorylation of the ERK protein in RBL-2H3 cells. This study demonstrated that Shechuang-Kushen Decoction inhibited compound 48/80-induced acute pruritus by inhibiting RBL-2H3 cell degranulation and the release of pruritus-related cytokines.

To explore the impact of different epiphytic tree species on the nutritional composition of Dendrobium officinale, this study focused on epiphytic D. officinale from eight tree species (i.e. Quercus glauca, Castanea spp., Liquidambar formosana, Schima superba, Myrica rubra, Albizia julibrissin, Benincasa hispida, and Juglans regia) as well as their corresponding barks from Anlong. In addition, four essential nutrients and selenium content in D. officinale, as well as eleven mineral elements in the barks, were measured and subjected to correlation analysis. Through the SPSS software, principal component analysis (PCA) was performed on the four nutrients and selenium elements in D. officinale medicinal materials to reflect the differences in the quality of D. officinale medicinal materials cultivated in different epiphytic tree species. As shown by the results, there were significant variations in the influence of different mineral elements in the barks on the nutritional composition of D. officinale. The contents of Se was 20.3-49.7 μg/kg, extract was 7.2%-11.5%, polysaccharide was 23.7%-36.8%, total flavonoid was 23.6-49.9 mg/g, free amino acid was 0.3-6.8 mg/g, respectively in 8 species of epiphytic D. officinale. The results of principal component analysis showed that the three main components which had great influence on the quality of D. officinale were Se, polysaccharide and free amino acid. The comprehensive scores from PCA indicated the quality ranking of the eight D. officinale medicinal materials as follows:Schima superba, Castanea spp., Albizia julibrissin, Liquidambar formosana, Myrica rubra, Juglans regia, Benincasa hispida, and Quercus glauca. Overall, the results of this study can provide scientific basis for the quality evaluation of D. officinale and the selection of its epiphytic tree species.

In order to study the difference of tolerance to waterlogging of commonly planted peanut varieties in China,the germination rate,SPAD value,root length,plant height,root biomass and other related indexes of different varieties were measured in a pot experiment at the seeding stage with normal moisture as the control group, and the waterlogging stress treatment was set for 48 hours,the waterlogging resistance of different peanut varieties was comprehensively evaluated to screen out peanut varieties with significant difference in waterlogging tolerance. The results showed that the morphological establishment in peanut seedling stage was inhibited to different degrees by waterlogging. Compared with the control groups,the root fresh weight,aboveground fresh weight,root biomass,aboveground biomass,and plant biomass of peanut under waterlogged conditions were decreased by 52.24%,45.43%,54.22%,37.84%,and 41.81%,respectively. Under waterlogging stress,the average coefficient of variation value of the above-ground morphological indicators was 32.48,while the average coefficient of variation value of the root morphological indicators reached 38.62. The relative values of root fresh weight,plant fresh weight,root length,plant height and SPAD value were used as indicators to evaluate the waterlogging-tolerant in peanut. The cluster analysis was conducted to classify 16 peanut varieties in this study into two categories of strong and weak waterlogging resistance,which laid a good theoretical foundation for further research on the waterlogging tolerance mechanism and cultivation of waterlogging-resistant varieties in peanut.

The unique morphological characteristics of Cunninghamia lanceolata var. Luotian make it an ideal plant type for forest trees, with high economic use value and great prospect for promotion and application, but its offspring determination is not perfect, and the genetic parameters of each trait are unknown, which is not conducive to the genetic improvement of C. lanceolata var. Luotian. In this study, the genetic variation of C. lanceolata var. Luotian was analyzed, so as to carry out the early selection of excellent lineages, in order to provide a theoretical basis for the conservation and selective breeding of weeping cedar germplasm resources. C. lanceolata var. Luotian was used as materials in this study, the growth differences and genetic rules of 3a and 4a tree height (TH) and diameter of breast height (DBH) different families were compared and analyzed, and the early selection of excellent family lines was carried out. The results showed that the average TH and DBH of 3a in the offspring were 3.11 m and 4.07 cm, and the average TH and DBH of 4a were 4.16 m and 5.35 cm, respectively. There were significant differences in growth traits between families at 3a and 4a (P<0.01). The phenotypic coefficient of variation ranged from 26.24%-41.83%, the genetic coefficient of variation was 5.37%-9.15%, the heritability range was 0.31-0.43 per plant, and the heritability range was 0.61-0.70 in the family lineage, all of which were controlled by a moderate degree of genetics. Among the different source families, the growth traits of the semi-sibling family of P. weeping were good, and the growth traits were maximum. According to the 15% enrollment rate, five excellent families were selected, namely 1-17, 1-21, 1-20, 2-3 and 3-6, and the average values of the selection were 3.43 m (3a TH), 4.74 cm (3a DBH), 4.64 m (4a TH) and 6.20 cm (4a DBH), were and the average genetic gain were 6.71%, 10.25%, 7.92% and 11.13%, respectively. The excellent family line bred can provide excellent germplasm material for the protection of weeping cedar germplasm resources and selective breeding.

The acceleration of urbanization processes will inevitably lead to alterations in regional ecological security patterns. These patterns are pivotal for achieving balanced regional economic development and ecological equilibrium. This study focuses on the Lijiang River Basin,a quintessential karst ecologically fragile region,and constructs its ecological security pattern using circuit theory within a framework of point (node,barrier point)-line (ecological network)-surface (ecological source). Ecological sources are pinpointed through ecological service value estimation and ecological sensitivity analysis. The ecological network is then established using circuit theory to optimize landscape patterns,thereby enhancing and actualizing ecological values within the Lijiang River Basin. The findings reveal that there are 12 ecological source sites in the Lijiang River Basin,covering an area of 1 624.71 km²,which constitutes 31% of the total basin area. Within the constructed ecological network model of the Lijiang River Basin,there exist 15 ecological corridors:seven significant and eight potentials,with the longest being 64.35 km. The landscape optimization in the Lijiang River Basin exhibits a “one belt two corridors three zones” pattern. This pattern can serve as an ecological optimization and restoration function tailored to local conditions,enhance landscape connectivity between each source site,provide a scientific foundation for constructing the ecological security pattern and ecological restoration in the Lijiang River Basin,and offer decision-making references for ecosystem regulation in the basin and high-quality development of Guilin City's National Sustainable Development Agenda Innovation Demonstration Zone.