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Coastal wetlands, vital hubs of economic activities, are under significant pressure, leading to the establishment of protected areas as a crucial conservation tool. Yet, uncertainties remain regarding the long-term effectiveness of these areas, especially in megacity cores subject to intense human disturbances. This study utilized long-term Landsat satellites monitoring to track the annual impact of protected areas on coastal wetland variations in the Guangzhou-Hong Kong-Macau Greater Bay Area from 1990 to 2020. Using annual classification datasets featuring 8 land cover/land use types with an average overall accuracy of 94.0±0.4 %, the research identified a significant increase in wetland areas before 2002, followed by a slight decline due to the exploitation of aquaculture and water bodies. Protected areas established for over seven years demonstrated a significantly positive impact on wetland restoration, underscoring the value of long-term conservation efforts. While mangrove protected areas showed strong recovery trends, other wetlands in developing regions continued to decline despite protection. This analysis indicated that the overall benefits of protected areas diminished when considering broader spatial contexts. These insights emphasized the need for adaptive management strategies to enhance the effectiveness of protected areas in urban environment.

Soil salinization is a widespread abiotic stress in China and one of the most critical factors affecting agricultural production and food security. γ-Aminobutyric acid (GABA) is a non-protein amino acid widely found in vertebrates, plants, and microorganisms, regulating the nervous system as well as plant defense systems. Mulberry is rich in GABA and exhibits extensive adaptability to various environments. In this study, we explored the possibility of alleviating salinity and alkalinity stress in mulberry seedlings using GABA and elucidating the intrinsic mechanisms by which GABA enhances salt-alkaline tolerance in mulberry trees through transcriptomic sequencing analysis. The results showed that 1 mM exogenous GABA enhanced the activities of mulberry seeds under saline-alkali stress, significantly increased the activities of POD and CAT (P < 0.01), reduced the level of reactive oxygen species and the content of malondialdehyde in mulberry seedlings and facilitated the growth and development of adventitious roots of mulberry. Transcriptomic analysis showed that GABA promoted the development and growth of adventitious roots of mulberry under saline-alkali stress by regulating the synthesis and modification of the cell wall, phytohormone signal transduction, and the conversion of starch and monosaccharides. Furthermore, the expression of pathogen pattern recognition receptors on the cell membrane of mulberry root system cells increased, enhancing the defense ability of mulberry root system cells. It is suggested that MYB, PME, SBT, EXP, DIR, POD, and the ARR family of transcription factors could be used as the target genes for further in-depth research.

Abstract Background Leaf coloration in plants, attributed to anthocyanin compounds, plays a crucial role in various physiological functions, and also for pharmaceutical and horticultural uses. However, the molecular mechanisms governing leaf coloration and the physiological significance of anthocyanins in leaves remain poorly understood. Results In this study, we investigated leaf color variation in two closely related mulberry genotypes, one with purplish-red young leaves (EP) and another with normal leaf color (EW). We integrated transcriptomic and metabolomic approaches to gain insights into the metabolic and genetic basis of purplish-red leaf development in mulberry. Our results revealed that flavonoid biosynthesis, particularly the accumulation of delphinidin-3-O-glucoside, is a key determinant of leaf color. Additionally, the up-regulation of CHS genes and transcription factors, including MYB family members, likely contributes to the increased flavonoid content in purplish-red leaves. Conclusion These findings enhance our understanding of the molecular mechanisms responsible for the purplish coloration observed in mulberry leaves and also offer supporting evidence for the hypothesis that anthocyanins serve a protective function in plant tissues until the processes of light absorption and carbon fixation reach maturity, thereby ensuring a balanced equilibrium between energy capture and utilization.

Background Limb salvage surgery is the preferred treatment for most malignant bone tumors, but postoperative infection treatment is very challenging. Simultaneously controlling infection and solving bone defects are clinical treatment challenges. Case Presentation Here we describe a new technique for treating bone defect infection after bone tumor surgery. An 8‐year‐old patient suffered an incision infection after osteosarcoma resection and bone defect reconstruction. In response, we designed her a personalized, anatomically matched, antibiotic‐loaded, bone cement spacer mold using 3D printing technology. The patient's infection was cured, and limb salvage was successful. In follow‐up, the patient had returned to normal postoperative chemotherapy and was able to walk with the help of a cane. There was no obvious pain in the knee joint. At 3 months after operation, the range of motion of the knee joint was 0°–60°. Conclusion The 3D printing spacer mold is an effective solution for treating the infection with large bone defect.

Scented green tea (Camellia sinensis) is a type of reprocessed green tea produced by scenting with flowers. To investigate the differences in the volatiles of scented green tea processed with four different flowers (Jasminum sambac, Osmanthus fragrans, Michelia alba, and Rosa rugosa), gas chromatography–ion mobility spectrometry (GC–IMS) and gas chromatography–mass spectrometry (GC–MS) were employed to detect and identify the volatile compounds in the four types of scented teas. GC–IMS and GC–MS identified 108 and 101 volatile compounds, respectively. The key characteristic volatile compounds, namely indole, linalool, β-myrcene, benzyl acetate, and ethyl benzoate (jasmine tea); cedrol, (E)-β-ionone, γ-decalactone, and dihydro-β-ionol (osmanthus tea); geraniol, phenylethyl alcohol, jasmone, methyl jasmonate, hexadecanoic acid, 4-ethyl-benzaldehyde, 2-methylbutyl hexanoate, and indole (michelia tea); and 3,5-dimethoxytoluene, (E)-β-ionone, and 2-methylbutyl hexanoate (rose tea), were identified through chemometric analysis combined with relative odor activity values (ROAVs) and sensory evaluation. This study provides new insights into the formation of aroma molecular fingerprints during green tea scenting with flowers, providing theoretical guidance for infusing distinct aroma characteristics into green tea during scented tea processing.

Premium green tea is a high-value agricultural product significantly influenced by its geographical origin, making it susceptible to food fraud. This study utilized nuclear magnetic resonance (NMR) spectroscopy to perform chemical fingerprint analysis on 78 Longjing tea (LJT) samples from both protected designation of origin (PDO) regions (Zhejiang) and non-PDO regions (Sichuan, Guangxi, and Guizhou) in China. Unsupervised algorithms and heatmaps were employed for the visual analysis of the data from PDO and non-PDO teas while exploring the feasibility of linear and nonlinear machine-learning algorithms in discriminating the origin of LJT. The findings revealed that the nonlinear model random forest (92.2%), exhibited superior performance compared to the linear model linear discriminant analysis (85.6%). The random forest model identified 15 key marker metabolites for the geographical origin of LJT, such as kaempferol glycoside, glutamine, and ECG. The results support the conclusion that the integration of NMR with machine-learning classification serves as an effective tool for the quality assessment and origin identification of LJT.

Abstract Bridges are a kind of infrastructure constructed to save urban space, to relieve traffic congestion, and to show urban modernisation. Therefore, bridge safety has become the focus of social attention. To know the structural condition of the bridge in real‐time, it is necessary to develop an intelligent and efficient bridge health monitoring system. However, the traditional bridge health monitoring system has poor real‐time performance, low efficiency, and a low degree of automation. The development of LoRa technology is expected to improve this situation. In this study, we designed a bridge‐distributed monitoring system based on LoRa technology, which has end nodes, relay nodes, a collect node, and a monitoring centre. In detail, the STM32H743XI processor is combined with the radio frequency SX1268 chip to form the above nodes. Furthermore, the end node contains the pulse vibration excitation circuit, and pick‐up signal conditioning circuit of the vibrating string crack metre, and the signal conditioning circuit of the temperature sensor AD590 and the humidity sensor HM1500LF. Finally, the data preprocessing of sensors and the communication performance of the two‐stage relay network of the LoRa module, which represent the key technologies to effectively and efficiently meet the real‐time monitoring of bridge conditions, are tested and discussed. This distributed monitoring system for the bridge can provide feasible solutions for the application field of bridge monitoring, which is helpful for the daily maintenance management and risk warning decision‐making of bridges.

Abstract In this paper, a novel robust distributed consensus control scheme based on event-triggered adaptive sliding mode control is proposed for multiagent systems with unknown disturbances in a leader-follower framework. First, an adaptive multivariate disturbance observer is utilized to compensate for the disturbance of each agent. Next, a distributed consensus control protocol is constructed via integral sliding mode control, in which a novel adaptive law is designed for the switching gain to overcome the unknown perturbations. An event-triggered strategy is designed to update the control input. Furthermore, the feasibility of the proposed scheme is rigorously analyzed by Lyapunov theory, and a lower bound expression for the inter-event time is derived to guarantee that Zeno behavior can be excluded. The proposed nonlinear consensus algorithm is remarkable in that it does not require any information about the bounds of the disturbances. Finally, compared with existing methods, the proposed algorithm is validated through detailed numerical simulations. In addition, the proposed algorithm is applied to a group of UAVs in this paper, and the results show that it has more application value.

Dedicated to meeting the growing demand for multi-agent collaboration in complex scenarios, this paper introduces a parameter-sharing off-policy multi-agent path planning and the following approach. Current multi-agent path planning predominantly relies on grid-based maps, whereas our proposed approach utilizes laser scan data as input, providing a closer simulation of real-world applications. In this approach, the unmanned aerial vehicle (UAV) uses the soft actor–critic (SAC) algorithm as a planner and trains its policy to converge. This policy enables end-to-end processing of laser scan data, guiding the UAV to avoid obstacles and reach the goal. At the same time, the planner incorporates paths generated by a sampling-based method as following points. The following points are continuously updated as the UAV progresses. Multi-UAV path planning tasks are facilitated, and policy convergence is accelerated through sharing experiences among agents. To address the challenge of UAVs that are initially stationary and overly cautious near the goal, a reward function is designed to encourage UAV movement. Additionally, a multi-UAV simulation environment is established to simulate real-world UAV scenarios to support training and validation of the proposed approach. The simulation results highlight the effectiveness of the presented approach in both the training process and task performance. The presented algorithm achieves an 80% success rate to guarantee that three UAVs reach the goal points.

Taking the external disturbances with unknown boundaries into consideration, this paper deals with the time-varying formation control problem of multiple quadrotors system based on static/dynamic event-triggered integral sliding mode control (ISMC) and adaptive sliding mode disturbance estimator (ASMDE). At first, two different position controllers, static event-triggered and dynamic event-triggered controllers, are proposed to deal with the time-varying formation problem of the multiple quadrotors system based on ISMC. The stability conditions are obtained through the Lyapunov theory. By the reverse proving method, it is also proved that there is no Zeno behavior in both static event-triggered position controller and dynamic event-triggered position controller. Then, the external disturbances are considered where the bounds of the perturbations are unknown. The ASMDE is applied to estimate the external disturbances, which increases the robustness of the multiple quadrotors system when applying the proposed methods. Next, the chattering free terminal sliding mode attitude controller for individual quadrotor is designed. Finally, the theoretical results are validated through numerical simulations.

Satellite and reanalysis precipitation estimates of high quality are widely used for hydrological modeling, especially in ungauged or data-scarce regions. To improve flood simulations by merging different precipitation inputs or directly merging streamflow outputs, this study comprehensively evaluates the accuracy and hydrological utility of nine corrected and uncorrected precipitation products (TMPA-3B42V7, TMPA-3B42RT, IMERG-cal, IMERG-uncal, ERA5, ERA-Interim, GSMaP, GSMaP-RNL, and PERSIANN-CCS) from 2006 to 2018 on a daily timescale using the Coupled Routing and Excess Storage (CREST) hydrological model in two flood-prone tributaries, the Beijiang and Dongjiang Rivers, of the Pearl River Basin, China. The results indicate that (1) all the corrected precipitation products had better performance (higher CC, CSI, KGE’, and NSCE values) than the uncorrected ones, particularly in the Beijiang River, which has a larger drainage area; (2) after re-calibration under Scenario II, the two daily merged precipitation products (NSCE values: 0.73–0.87 and 0.69–0.82 over the Beijiang and Dongjiang Rivers, respectively) outperformed their original members for hydrological modeling in terms of BIAS and RMSE values; (3) in Scenario III, four evaluation metrics illustrated that merging multi-source streamflow simulations achieved better performance in streamflow simulation than merging multi-source precipitation products; and (4) under increasing flood levels, almost all the performances of streamflow simulations were reduced, and the two merging schemes had a similar performance. These findings will provide valuable information for improving flood simulations and will also be useful for further hydrometeorological applications of remote sensing data.

Nitrate (NO3−) accumulation in recirculating aquaculture systems (RASs) with high stocking densities presents a problem for reared animals and the environment. The use of a biodegradable polymer as organic carbon for heterotrophic denitrification exhibits good performance for NO3− removal from wastewater. A comparison of NO3−–N removal efficiency and bacterial properties using polycaprolactone (PCL) and poly (3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) as carbon sources to treat aquaculture water was conducted for a 102-day period. The results indicated that the NO3−–N removal rates of 0.27 ± 0.07 and 0.19 ± 0.05 g/L per day, respectively, could be achieved with influent concentrations ranging from 81.1 to 132.75 mg/L and a flow rate of 1 L/h. The removal of NO3−–N versus consumed PCL (1:1 w/w) was significantly higher than that versus consumed PHBV (0.3:1 w/w) (P

In this work, micromagnetic simulations demonstrate that a steady oscillation mode accompanied by magnetic domain splitting and the creation and annihilation of meron can be excited by spin-polarized currents. It is found that the in-plane magnetic anisotropy and Dzyaloshinskii-Moriya interaction (DMI) have a greater influence on the oscillation frequency. The oscillation frequency can vary from 3 GHz to 31 GHz by controlling anisotropy strength under a fixed current density. By changing DMI strength, the oscillation frequency varies from 9 to 13.6 GHz and from 29.7 to 37 GHz. Compared with ferromagnetic skyrmion-based spin-torque oscillators (STOs), the STOs based on this magnetic domain and meron further increase the oscillation frequency. Our results may provide theoretical support for the research and development of future high-frequency STOs.

Mulberry leaves at different positions are different in photosynthetic rate, nutrient substance and feeding impact to silkworms. Here, we investigated the proteomic differences of the first (L1), sixth (L6), and twentieth (L20) mulberry leaves at different stem positions (from top to the base) using a label-free quantitative proteomics approach. L1 contained less developed photosynthetic apparatus but was more active in protein synthesis. L20 has more channel proteins and oxidoreductases relative to L6. Proteins that detected in all measured leaves were classified into three groups according to their expression patterns in L1, L6, and L20. The protein group that displayed the maximum amount in L6 has the highest possibility that function related to photosynthesis. Nine function unknown proteins belong to this group were further analyzed in the light responsive expression, evolutionary tree and sub-cellular localization analysis. Based on the results, five proteins were suggested to be involved in photosynthesis. Taken together, these results reveal the molecular details of different roles of mulberry leaves at different developmental stages and contribute to the identification of five proteins that might function related to photosynthesis.

Prefabricated buildings are gradually gaining popularity in China because of their lower costs, shorter construction periods, and environmental friendliness. However, compared with traditional buildings, prefabricated buildings require coordination and management of processes such as component production, transportation and storage, and on-site component assembly. Therefore, managing the quality of prefabricated buildings is complex. In order to manage the quality of prefabricated buildings better, it is necessary to study the quality systems of prefabricated buildings. Firstly, by analyzing the logical relationship of each stage of the prefabricated buildings, we identified the key stages. Then, the main quality control factors that affect the qualities and relationships of the main quality control factors were derived by combining relevant building codes, literature studies, and project experiences; a network model of quality control factors of prefabricated buildings (NQPB) was constructed. Secondly, the complex network theory was applied to analyze the node degree, betweenness centrality, clustering coefficient, and other indicators of the NQPB to derive 17 key quality control factor nodes. The importance of the 17 key nodes in the quality management of prefabricated buildings was verified through a simulation. Next, the NQPB of Project A was constructed and analyzed, and five key nodes were identified. This demonstrates how the NQPB method can be applied to a specific prefabricated building project. Moreover, Project A was used as a case study to compare fault tree analysis (FTA), fishbone diagram, and 4MIE methods with the NQPB method to derive the potential advantages of the NQPB method. Finally, further analysis led to recommendations for quality management of prefabricated building projects in general as well as for Project A. This study can be used as a reference for quality control and assurance managers in all stages of prefabricated buildings and provide new ideas and methods for the quality analysis and management of prefabricated buildings.

Effective integration and wide sharing of geospatial data is an important and basic premise to facilitate the research and applications of geographic information science. However, the semantic heterogeneity of geospatial data is a major problem that significantly hinders geospatial data integration and sharing. Ontologies are regarded as a promising way to solve semantic problems by providing a formalized representation of geographic entities and relationships between them in a manner understandable to machines. Thus, many efforts have been made to explore ontology-based geospatial data integration and sharing. However, there is a lack of a specialized ontology that would provide a unified description for geospatial data. In this paper, with a focus on the characteristics of geospatial data, we propose a unified framework for geospatial data ontology, denoted GeoDataOnt, to establish a semantic foundation for geospatial data integration and sharing. First, we provide a characteristics hierarchy of geospatial data. Next, we analyze the semantic problems for each characteristic of geospatial data. Subsequently, we propose the general framework of GeoDataOnt, targeting these problems according to the characteristics of geospatial data. GeoDataOnt is then divided into multiple modules, and we show a detailed design and implementation for each module. Key limitations and challenges of GeoDataOnt are identified, and broad applications of GeoDataOnt are discussed.

Mulberry (Morus alba L.) is a perennial woody plant with significant economic benefits and ecological value. The floral character of mulberry has an important impact on the yield and quality to its fruits and leaves. However, little is known about the molecular mechanism of mulberry floral differentiation still now. The transcriptome data were obtained via Illumina HiSeq high-throughput sequencing from male and female inflorescences of the monoecious mulberry. A total of 26.21 Gb clean data were obtained, and as many as 100,177 unigenes with an average length of 821.66 bp were successfully assembled. In comparative-omics analysis, 1717 differentially expressed genes (DEGs) were identified between male and female flowers and only a quarter of the DEGs were highly expressed in female flowers. The KEGG pathway enrichment analysis revealed that DEGs were involved in glucose and lipid metabolism, hormone signal transduction, and the regulation of related transcription factors. In addition, many DEGs related to flower development and plant sex differentiation have also been detected, such as PMADS1/2, AGAMOUS, FLOWERING LOCUS T (FT), APETALA 2 (AP2), TASSELSEED2 (TS2), and ARABIDOPSIS RESPONSE REGULATOR 17 (ARR17). Finally, the expression patterns of selected 20 DEGs were validated by q-PCR and the results showed that the transcriptome data were highly reliable. This study shows that the differentiation of male and female flowers of mulberry is affected and regulated by multiple factors, with transcription factors and hormone signals playing a key role. Briefly, the current data provide comprehensive insights into the mulberry tree’s floral differentiation as well as a bioinformatics framework for the development of molecular breeding of mulberry.

Surface meshes extracted from sparse medical images contain surface artifacts, there will produce serious distortion and generate numerous narrow triangle meshes. In order to eliminate the impact of the above factors, this paper presents a novel method for generating smooth and adaptive meshes from medical image datasets. Firstly, extracting the stack of contours by means of image segmentation and translating the contours into point clouds. The improved Multi-Level Partition of Unity (MPU) implicit functions are used to fit the point clouds for creating the implicit surface. Then, sampling implicit surface through dynamic particle systems based on Gaussian curvature, dense particles sampling in the high curvature region, sparse particles sampling in the low curvature region. Finally, generating triangle meshes based on particle distribution by using the Delaunay triangulation algorithm. Experimental results show that the proposed method can generate high-quality triangle meshes with distributed adaptively and have a nice gradation of triangle mesh density on the surface curvature.

Linked Data is known as one of the best solutions for multisource and heterogeneous web data integration and discovery in this era of Big Data. However, data interlinking, which is the most valuable contribution of Linked Data, remains incomplete and inaccurate. This study proposes a multidimensional and quantitative interlinking approach for Linked Data in the geospatial domain. According to the characteristics and roles of geospatial data in data discovery, eight elementary data characteristics are adopted as data interlinking types. These elementary characteristics are further combined to form compound and overall data interlinking types. Each data interlinking type possesses one specific predicate to indicate the actual relationship of Linked Data and uses data similarity to represent the correlation degree quantitatively. Therefore, geospatial data interlinking can be expressed by a directed edge associated with a relation predicate and a similarity value. The approach transforms existing simple and qualitative geospatial data interlinking into complete and quantitative interlinking and promotes the establishment of high-quality and trusted Linked Geospatial Data. The approach is applied to build data intra-links in the Chinese National Earth System Scientific Data Sharing Network (NSTI-GEO) and data -links in NSTI-GEO with the Chinese Meteorological Data Network and National Population and Health Scientific Data Sharing Platform.

Recently, the perpendicular magnetic tunnel junctions (p-MTJs) arouse great interest because of its unique features in the application of spin-transfer-torque magnetoresistive random access memory (STT-MRAM), such as low switching current density, good thermal stability and high access speed. In this paper, we investigated current induced switching (CIS) in ultrathin MgO barrier p-MTJs with dimension down to 50 nm. We obtained a CIS perpendicular tunnel magnetoresistance (p-TMR) of 123.9% and 7.0 Ω·μm2 resistance area product (RA) with a critical switching density of 1.4×1010 A/m2 in a 300 nm diameter junction. We observe that the extrinsic breakdown mechanism dominates, since the resistance of our p-MTJs decreases gradually with the increasing current. From the statistical analysis of differently sized p-MTJs, we observe that the breakdown voltage (Vb) of 1.4 V is 2 times the switching voltage (Vs) of 0.7 V and the breakdown process exhibits two different breakdown states, unsteady and steady state. Using Simmons’ model, we find that the steady state is related with the barrier height of the MgO layer. Furthermore, our study suggests a more efficient method to evaluate the MTJ stability under high bias rather than measuring Vb. In conclusion, we developed well performant p-MTJs for the use in STT-MRAM and demonstrate the mechanism and control of breakdown in nano-scale ultrathin MgO barrier p-MTJs.

This paper addresses chaos suppression of the mechanical centrifugal flywheel governor system with output constraint and fully unknown parameters via adaptive dynamic surface control. To have a certain understanding of chaotic nature of the mechanical centrifugal flywheel governor system and subsequently design its controller, the useful tools like the phase diagrams and corresponding time histories are employed. By using tangent barrier Lyapunov function, a dynamic surface control scheme with neural network and tracking differentiator is developed to transform chaos oscillation into regular motion and the output constraint rule is not broken in whole process. Plugging second-order tracking differentiator into chaos controller tackles the “explosion of complexity” of backstepping and improves the accuracy in contrast with the first-order filter. Meanwhile, Chebyshev neural network with adaptive law whose input only depends on a subset of Chebyshev polynomials is derived to learn the behavior of unknown dynamics. The boundedness of all signals of the closed-loop system is verified in stability analysis. Finally, the results of numerical simulations illustrate effectiveness and exhibit the superior performance of the proposed scheme by comparing with the existing ADSC method.

In this paper, chaos control is proposed for the output- constrained system with uncertain control gain and time delay and is applied to the brushless DC motor. Using the dynamic surface technology, the controller overcomes the repetitive differentiation of backstepping and boundedness hypothesis of pre-determined control gain by incorporating radial basis function neural network and adaptive technology. The tangent barrier Lyapunov function is employed for time-delay chaotic system to prevent constraint violation. It is proved that the proposed control approach can guarantee asymptotically stable in the sense of uniformly ultimate boundedness without constraint violation. Finally, the effectiveness of the proposed approach is demonstrated on the brushless DC motor example.

The spin-torque induced magnetization precession dynamics are studied in a spin-valve with a tilted spin polarizer. Macrospin simulations demonstrate that the frequency of precession state depends both on the external DC current and the intrinsic parameters of devices such as the tilted angle of spin polarizer, the damping factor and saturation magnetization of the free layer. The dependence role of those parameters is characterized by phase diagrams. An analytical model is presented, which can successfully interpret the features of precession frequency.