Spatial Structure Characteristics Research Articles

A Novel Approach to Optimize Key Limitations of Azure Kinect DK for Efficient and Precise Leaf Area Measurement

Maize leaf area offers valuable insights into physiological processes, playing a critical role in breeding and guiding agricultural practices. The Azure Kinect DK possesses the real-time capability to capture and analyze the spatial structural features of crops. However, its further application in maize leaf area measurement is constrained by RGB–depth misalignment and limited sensitivity to detailed organ-level features. This study proposed a novel approach to address and optimize the limitations of the Azure Kinect DK through the multimodal coupling of RGB-D data for enhanced organ-level crop phenotyping. To correct RGB–depth misalignment, a unified recalibration method was developed to ensure accurate alignment between RGB and depth data. Furthermore, a semantic information-guided depth inpainting method was proposed, designed to repair void and flying pixels commonly observed in Azure Kinect DK outputs. The semantic information was extracted using a joint YOLOv11-SAM2 model, which utilizes supervised object recognition prompts and advanced visual large models to achieve precise RGB image semantic parsing with minimal manual input. An efficient pixel filter-based depth inpainting algorithm was then designed to inpaint void and flying pixels and restore consistent, high-confidence depth values within semantic regions. A validation of this approach through leaf area measurements in practical maize field applications—challenged by a limited workspace, constrained viewpoints, and environmental variability—demonstrated near-laboratory precision, achieving an MAPE of 6.549%, RMSE of 4.114 cm², MAE of 2.980 cm², and R² of 0.976 across 60 maize leaf samples. By focusing processing efforts on the image level rather than directly on 3D point clouds, this approach markedly enhanced both efficiency and accuracy with the sufficient utilization of the Azure Kinect DK, making it a promising solution for high-throughput 3D crop phenotyping.

Optimization of Development Strategies and Injection-Production Parameters in a Fractured-Vuggy Carbonate Reservoir by Considering the Effect of Karst Patterns: Taking C Oilfield in the Tarim Basin as an Example

The spatial structural characteristics of fractured-vuggy units vary greatly in different karst patterns, which significantly influence the study of remaining oil distribution patterns in ultra-deep fractured-vuggy reservoirs and the determination of the most efficient development strategies. However, few numerical simulation studies have focused on improving water and gas injection in fractured-vuggy reservoirs by considering the effect of karst patterns. By taking a typical fractured-vuggy reservoir in C oilfield in Tarim Basin, China as an example, the development dynamic characteristics of eight typical fractured-vuggy units in three different karst patterns are analyzed, and based on the newly proposed numerical simulation method of fluid vertical equilibrium, the residual oil reservoir distribution in different karst pattern fractured-vuggy units are studied, and the effects of fracture-vuggy karst patterns on the development characteristics, on the remaining oil morphology pattern, on the development strategies, and on the injection-production parameters are explored. This study shows that for different karst patterns fractured-vuggy units, the complexity of spatial structure, reserve scale, and oil-water relationship aggravates the heterogeneity of reservoirs and results in substantial differences in the development of dynamic patterns. In the northern facing karst fractured-vuggy units, there are two main types of remaining oil: well-spacing type and local-blocking type, and the reasonable development strategies are affected by reservoir morphology and the connectivity of structure patterns. Attic-type remaining oil mainly occurs in platform margin overlay and fault-controlled karst fractured-vuggy units. In the southern fault-controlled karst area, the remaining oil is mostly found along the upper part, and periodic gas injection or N2 huff-n-puff is recommended with priority for potential tapping. The fractured-vuggy karst patterns show a significant influence on the optimal level of injection-production parameters for improving the development of gas injection development strategies. The ideas of improving water injection and gas injection for fracture-vuggy reservoirs proposed in this paper also provide a good reference to further improve water control and increase oil production in other similar carbonate reservoirs.

Hierarchical partition of urban land-use units by unsupervised graph learning from high-resolution satellite images

ABSTRACT Urban land use information can be effectively extracted from high-resolution satellite images for many urban applications. A significant challenge remains the accurate partition of fine-grained land-use units from these images. This paper presents a novel method for deriving these units based on unsupervised graph learning techniques using high-resolution satellite images and open street boundaries. Our method constructs a graph to represent spatial relations between land cover objects as graph nodes within a street block. These nodes are characterized by spatial composition and structure features of their surrounding neighborhood. We then apply unsupervised graph learning to partition the graph into subgraphs, which represent communities spatially bounded by street boundaries and correspond to land use units. Next, a graph neural network is used to extract deep structural features for land use classification. Experiments were conducted using high-resolution satellite images from the cities of Fuzhou and Quanzhou, China. Results showed that our method surpassed traditional grid and street block techniques, improving land use classification accuracy by 24% and 9%, respectively. Furthermore, it achieved classification results comparable to those using reference land use units, with an overall accuracy of 0.87 versus 0.89.

In Ukrainian

Alkali metal hydroxides form layered crystals, their structure gradually becomes more complicated as the size of metal cations increases. In this work, a systematic quantum chemical analysis of the spatial structure and energy characteristics, as well as of vibrational spectra and thermodynamic parameters of molecular models for potassium hydroxide consisting of 2 – 20 formula units is performed. The structure and properties of the considered molecular models for potassium hydroxide were studied by the second-order Möller – Plesset perturbation theory method with the valence-split basis set 6 31G(d,p) using the PC GAMESS software package. In the potassium hydroxide molecule, the theoretical interatomic distances are K–O – 2.2212 Å, O–H – 0.9626 Å, respectively. During dimer and tetramer formation, the corresponding values gradually increase. Interatomic K–O distances within one bilayer block of molecular models vary from 2.62 to 2.96 Å, and those between blocks – 3.15 Å. In potassium hydroxide crystals, two-layer blocks are bound to each other by zigzag hydrogen bonds 3.35 Å long. Molecular models reproduce such bonds. The KOH molecule in the IR spectrum has 3 bands corresponding to the stretching vibrations of О–Н (3610 cm-1), K–O (408 cm-1) and bending vibration of K–O–H (300 cm 1). The calculation gives 3806, 494 and 372 cm-1, respectively. The calculated IR spectra of molecular models with interblock hydrogen bonds indicate the presence of absorption bands in different ranges: around 3800 – 3900 cm-1 (stretching vibrations of OH groups), in the range of 400 – 800 cm-1 (bending vibrations of OH groups). The cohesion energy of potassium hydroxide is 194.4 kJ/mol. Calculations of this quantity for clusters give its value in the range of 178.5 – 217.2 kJ/mol. An analysis of the calculated geometric and energy characteristics of the considered models indicates their stability and closeness to the experimental ones. These models can be used in the study of various processes that occur with the participation of potassium hydroxide.

Dynamics in Land Cover and Landscape Patterns of Myanmar: A Three-Decade Perspective (1990–2020)

A comprehensive scientific assessment of the dynamic changes in land cover and landscape patterns in Myanmar, considering both human activities and natural factors such as climate change, is essential for a thorough understanding of the transformations in the country’s ecological environment. This assessment also provides data-driven insights into the complex interactions between humans, climate, and the environment. This study aims to examine the dynamic changes in land cover in Myanmar over a thirty-year period from a comprehensive perspective. This paper, based on the MLC30 land cover dataset for Myanmar from 1990 to 2020, employs land use dynamic degree and land use transition matrix to analyze the extent and process of land cover changes in Myanmar. Furthermore, using landscape pattern indicators, the paper explores the changes in the spatial structural characteristics of land cover in Myanmar at both the patch scale and the landscape scale. The results indicate the following: (a) Areas with significant land cover changes are primarily located in the eastern, southeastern, and southwestern regions bordering China, Laos, and Thailand, as well as the coastal areas, with the change intensity from 2000 to 2020 being notably higher than before 2000. (b) Myanmar’s cultivated land, artificial surfaces, and water bodies show an expanding trend, with cultivated land expansion mainly at the expense of forests, while the increase in artificial surfaces and water bodies is through the conversion of the existing cultivated land. (c) Myanmar’s landscape patterns remained stable from 1990 to 2000. However, after 2000, the land cover has shown a clear trend towards fragmentation and spatial distribution dispersion, especially for the dominant forest and cultivated land types. Despite Myanmar’s rapid economic development, the trend toward the fragmentation and irregularization of cultivated land patches indicates a lack of attention to cultivated land use and planning. The reduction and fragmentation of forest areas have led to a decline in ecological connectivity, posing risks of ecological environment deterioration. Consequently, Myanmar must prioritize scientific land use planning and the rational allocation of land resources to foster the sustainable development of agriculture and the protection of natural ecosystems.

Research on the gradual change model of vibration characteristics of spatial steel truss structures under fatigue loading

A bottom–up multi-scale performance transfer chain is developed to ascertain the gradual change in the law of vibration characteristics in spatial steel truss structures under fatigue loading. This chain derives the interrelationship between vibration characteristics and the performance of each structural component. Based on the evolution characteristics of the performance model of the member scale in the transfer chain, a new analytical model of the shape function is established. Building on these foundations, a refined model was formulated to depict the gradual change of vibration characteristics in spatial steel truss systems under fatigue loading. This model integrates the cumulative damage rule, the structural residual performance rule, the finite element method and other pertinent theories. The gradual change in vibration characteristics of a steel truss girder bridge subjected to train loads was analyzed using a self-developed three-dimensional finite element analysis framework tailored explicitly for assessing gradual change issues in spatial steel truss structures. The analysis results indicate that the effective residual cross-sectional area and the structural frequency continuously decline as the service time increases. This comprehensive study advances the understanding of fatigue-induced changes in spatial steel truss structures. It offers analytical perspectives for predicting and mitigating the long-term impacts of dynamic loads on large-scale infrastructural components.

Anti-tumour Activity of 1, 3- Dimethyl Acridones Against Ehrlich Ascites Carcinoma

We report here in vitro and in vivo antitumour activity study of a series of 1,3-dimethyl-N10 substituted acridone derivatives. All the molecules have been designed on the basis of the presence of specific recognition patterns consisting of electron donor, carbons. Chloro groups with precise spatial separation and structural features (lipophilicity, positive charge at neutral pH and presence of aromatic rings). In vitro and in vivo antitumour effects have been demonstrated against EAC cell-lines. Compounds 2,7,8,9,12,13,14 and 15 exhibited good antitumour activity when compared to Vincristine and compound 14 (β-hydroxy ethyl) piperazine derivative with four carbon spacer exhibited good antitumour activity.

Valuing the spatial structure and influencing factors of urban innovation from the perspective of social networks

ABSTRACT Valuing the spatial structural characteristics and evolution mechanisms of urban innovation networks (UIN) is crucial for promoting regional innovation development. This study constructs the UIN for the Yangtze River Economic Belt (YREB) and examines its spatial structural characteristics and driving factors from a social network perspective. The results indicate that: First, the network relationships within the urban innovation ecosystem in YREB continue to expand, enhancing stability, synergy and connectivity. Second, interaction relationships dominate the network, establishing distinct block functions, with provincial capitals serving as core nodes. Third, YREB can be divided into four blocks: the main inflow block, the main outflow block, the bidirectional spider block and the agent block, each exhibiting unique roles within the network. Fourth, increasing network density and connectedness while reducing hierarchy and enhancing efficiency positively impacts urban innovation. Strengthening node centrality characteristics is beneficial for promoting urban innovation. Lastly, proximity is conducive to the formation of UIN. Digital technology and traffic integration are replacing geographical and institutional factors, becoming key elements in the formation of UIN. Therefore, we propose optimising the spatial structure of UIN and shifting the driving paradigm from geographic and institution proximity to the digital technology and traffic proximity.

Characterizing the spatial correlation network structure and impact mechanism of carbon emission efficiency: Evidence from China's transportation sector

Numerous countries and regions are actively seeking to reduce carbon emissions through policy guidance and technological innovation. In this process, balancing economic development with environmental protection and achieving synergistic carbon reduction between regions pose challenges for policymakers and the academic community alike. This study analyzes data from 30 provinces in China over the period from 2005 to 2020, employing the SBM-DEA, block model, and the Exponential Random Graph Models (ERGM) to explore the spatial association network structure characteristics of carbon emission efficiency and its driving factors. The findings indicate that: the carbon emission efficiency of the transportation industry is generally on an upward trend, with the eastern region having the highest carbon emission efficiency; the spatial association network exhibits a “dense in the east, sparse in the west” pattern; the block model demonstrates clear inter-regional carbon emission transfer behaviors; the result of ERGM shows that factors such as the level of economic development and population density significantly affect the network structure. The macro-micro individual analysis framework for the carbon emission efficiency network fills the theoretical gap in the context of the digital economy, providing a scientific basis and decision-making reference for policymakers when formulating and optimizing carbon reduction policies, which holds significant theoretical and practical value.

Characteristics of Spatial Correlation Network Structure and Carbon Balance Zoning of Land Use Carbon Emission in the Tarim River Basin

An accurate understanding of the structure of spatial correlation networks of land use carbon emissions (LUCEs) and carbon balance zoning plays a guiding role in promoting regional emission reductions and achieving high-quality coordinated development. In this study, 42 counties in the Tarim River Basin from 2002 to 2022 were chosen as samples (Corps cities were excluded due to missing statistics). The LUCE spatial correlation network characteristics and carbon balance zoning were analyzed by using the Ecological Support Coefficient (ESC), Social Network Analysis (SNA), and Spatial Clustering Data Analysis (SCDA), and a targeted optimization strategy was proposed for each zone. The results of the study indicate the following: (1) The LUCEs showed an overall upward trend, but the increase in LUCEs gradually slowed down, presenting a spatial characteristic of “high in the mid-north and low at the edges”. In addition, the ESC showed an overall decreasing trend, with a spatial characteristic opposite to that of the LUCEs. (2) With an increasingly close spatial LUCE correlation network in the Tarim River Basin, the network structure presented better accessibility and stability, but the individual network characteristics differed significantly. Aksu City, Korla City, Bachu County, Shache County, Hotan City, and Kuqa City, which were at the center of the network, displayed a remarkable ability to control and master the network correlation. (3) Based on the carbon balance analysis, the counties were subdivided into six carbon balance functional zones and targeted synergistic emission reduction strategies were proposed for each zone to promote fair and efficient low-carbon transformational development among the regions.

Construction methods and latest applications of kidney cancer organoids.

Renal cell carcinoma (RCC) is one of the deadliest malignant tumors. Despite significant advances in RCC treatment over the past decade, complete remission is rarely achieved. Consequently, there is an urgent need to explore and develop new therapies to improve the survival rates and quality of life for patients. In recent years, the development of tumor organoid technology has attracted widespread attention as it can more accurately simulate the spatial structure and physiological characteristics of tumors within the human body. In this review, we summarize the main methods currently used to construct kidney cancer organoids, as well as their various biological and clinical applications. Furthermore, combining organoids with other technologies, such as co-culture techniques and microfluidic technologies, can further develop organoids and address their limitations, creating more practical models. This approach summarizes the interactions between different tissues or organs during tumor progression. Finally, we also provide an outlook on the construction and application of kidney cancer organoids. These rapidly evolving kidney cancer organoids may soon become a focal point in the development of in vitro clinical models and therapeutic research for kidney cancer.

Continuous-Time Dynamic Graph Networks Integrated with Knowledge Propagation for Social Media Rumor Detection

The proliferation of the Internet and mobile devices has made it increasingly easy to propagate rumors on social media. Widespread rumors can incite public panic and have detrimental effects on individuals. In recent years, researchers have found that both the spatial structure of rumor diffusion and the temporal features of propagation can be effective in identifying rumors. However, existing methods tend to focus on either spatial structure or temporal information in isolation, and few models can effectively capture both types of information. Additionally, most existing methods treat continuously changing temporal information as static snapshots, neglecting the precise timing of propagation. Moreover, as users repost and comment, background knowledge associated with the posts also evolves dynamically, which is often ignored. To address these limitations, we propose CGNKP (Continuous-time Dynamic Graph Networks integrated with Knowledge Propagation), a model that jointly captures the spatial structure and continuous-time features of post propagation to fully understand the dynamics of background knowledge. Specifically, we introduce a novel method for encoding continuous-time dynamic graphs, modeling the propagation process through two dynamic graphs: a temporal propagation graph (for posts diffusion) and a temporal knowledge graph (for knowledge diffusion). Extensive experiments on real-world datasets demonstrate that CGNKP significantly outperforms multiple strong baselines, achieving accuracies of 0.861 on the Twitter15 dataset and 0.903 on the Twitter16 dataset.

Leveraging computer-aided design and artificial intelligence to develop a next-generation multi-epitope tuberculosis vaccine candidate

BackgroundTuberculosis (TB) remains a global public health challenge. The existing Bacillus Calmette–Guérin vaccine has limited efficacy in preventing adult pulmonary TB, necessitating the development of new vaccines with improved protective effects. MethodsComputer-aided design and artificial intelligence technologies, combined with bioinformatics and immunoinformatics approaches, were used to design a multi-epitope vaccine (MEV) against TB. Comprehensive bioinformatics analyses were conducted to evaluate the physicochemical properties, spatial structure, immunogenicity, molecular dynamics (MD), and immunological characteristics of the MEV. ResultsWe constructed a MEV, designated ZL12138L, containing 13 helper T lymphocyte epitopes, 12 cytotoxic T lymphocyte epitopes, 8 B-cell epitopes, as well as Toll-like receptor (TLR) agonists and helper peptides. Bioinformatics analyses revealed that ZL12138L should exhibit excellent immunogenicity and antigenicity, with no toxicity or allergenicity, and had potential to induce robust immune responses and high solubility, the immunogenicity score was 4.14449, the antigenicity score was 0.8843, and the immunological score was 0.470. Moreover, ZL12138L showed high population coverage for human leukocyte antigen class I and II alleles, reaching 92.41% and 90.17%, respectively, globally. Molecular docking analysis indicated favorable binding affinity of ZL12138L with TLR-2 and TLR-4, with binding energies of −1173.4 and −1360.5 kcal/mol, respectively. Normal mode analysis and MD simulations indicated the stability and dynamic properties of the vaccine construct. Immune simulation predictions suggested that ZL12138L could effectively activate innate and adaptive immune cells, inducing high levels of Type 1 T helper cell cytokines. ConclusionsThis study provides compelling evidence for ZL12138L as a promising TB vaccine candidate. Future research will focus on experimental validation and further optimization of the vaccine design.

Integrating path signature and pen-tip trajectory features for online handwriting Yi text recognition

Recognizing online handwriting Yi text is crucial for recording and preserving Yi literature. However, the scarcity of online handwriting Yi text datasets has limited relevant research, impeding the process of Yi informatization. In this work, we use synthetic data to train models, and an Online Handwriting Yi Text Recognition Network (YTRN) is proposed, which extracts robust character features to address the gap between synthetic and real data. YTRN adeptly learns the spatial structure features from path signature feature maps and captures trajectory features from the pen-tip trajectories. Subsequently, an innovative adaptive feature fusion module integrates these two sets of features to yield more comprehensive and robust character representations. Experiments on our newly collected Yi-OLHWDB2.0 dataset demonstrate that our method outperforms previous approaches, achieving an impressive 95.67% accuracy. This highlights the model’s effectiveness in extracting comprehensive and robust features from path signature maps and pen-tip trajectories, significantly enhancing recognition accuracy and generalization.

Connections and Spatial Network Structure of the Tourism Economy in Beijing–Tianjin–Hebei: A Social Network Perspective

In the context of the coordinated development of Beijing–Tianjin–Hebei, examining the spatial connections and network structure characteristics of the tourism economy holds substantial theoretical and practical significance. This study constructs a tourism economic quality evaluation index system from four dimensions and optimizes the tourism economic gravity model by incorporating time distance and weight factors to dynamically simulate the intensity of tourism economic spatial connection (ITESC). Based on the new perspective of relational data, social network analysis is employed to investigate the spatial correlation network characteristics and group structure changes in the tourism economy within the Beijing–Tianjin–Hebei urban agglomeration (BTHUA). The results demonstrate a significant improvement in overall tourism economic quality from 2013 to 2022, with the average quality increasing from 0.128 in 2013 to 0.157 in 2022. Tourism economic spatial connections are prevalent among cities, exhibiting heterogeneous characteristics, characterized by density in the north and relatively sparse in the south. The network cohesion of ITESC strengthens, and the network structure evolves from a highly centralized to a multi-centralized configuration, with increasingly frequent cooperation and interaction in tourism between cities. Beijing, Tianjin, and Shijiazhuang occupy central positions in the network, exhibiting strong ITESC and a high degree of connectivity, generating a significant “trickle-down effect” on peripheral cities. Consequently, strengthening ITESC between peripheral cities and core areas, as well as enhancing the bidirectional spillover of tourism development between cities, is crucial for the future sustainable and coordinated development of tourism in the BTHUA.

Insights into the Vibration Characteristics of Spatial Radial Gate Affected by Fluid–Structure Interaction

Radial gate, a spatial frame structure, is the key factor to control water discharge in dam structure and storm surge barriers. However, the fluid-induced vibration (FIV) problem always occurs owing to fluctuation loads exerted on the gate, threatening the safety of hydropower stations. In this work, two fluid–structure interaction (FSI) modal analysis methods—the coupled acoustics–structure method and the added-mass method—are provided. Further, a comprehensive investigation on the vibration characteristics of the spatial radial gate, considering spatial structural characteristics and the FSI effect, is conducted. The numerical results revealed that the feasibility of the proposed coupled acoustics–structure method in analyzing FSI modal analysis was demonstrated; moreover, a reasonable length of the fluid domain in front of the skinplate existed for efficient computation. Meanwhile, through the added-mass method, the rational added-mass discount factor of hydrodynamic loads obtained from the Westergaard formula was provided. The FSI effect induced whole-gate rotation vibration streamwise around trunnion pins, significantly reducing the gate’s fundamental vibration frequency. In addition, three typical dynamic-instability vibration patterns of radial gates were presented. These patterns were affected by spatial structural characteristics and FSI. It was demonstrated that the struts and skinplate coupled bending–torsional vibration would cause the radial gate frame structure to fail catastrophically. The proposed insights can provide guidelines of vibration characteristics analysis of the radial gate submerged in flow water in reservoir and storm surge barriers.

Identification and Spatial Characterization of suburban areas in Chengdu

As transitional territories where urban and rural functions interpenetrate, suburban areas have multiple values such as recreation, ecology, agriculture and landscape. Promoting their benign development is the key to realizing global sustainable development of urban and rural areas. Planning control is based on precisely defining the spatial extent of suburban areas, and understanding suburban area differentiation and its driving forces scientifically is a key component in raising the bar for focused planning. However, there are currently few research on fine-scale quantitative identification of suburban regions because of their spatial complexity, ambiguous boundaries, and structural dynamic features. Based on this, this paper develops a multi-dimensional identification index system for suburban areas by using multi-source big data and remote sensing information technology, employs the random forest model and the K-Medoids clustering algorithm, identifies the distribution of suburban areas and their subdivided types, analyzes spatial differentiation characteristics, and conducts empirical research using Chengdu City as an example. The findings demonstrate that: (1) The majority of suburban areas in Chengdu City are concentrated in groups or belts surrounding the urban centers of various districts and counties, and the higher the functional class of a district or county, the denser the distribution of suburban areas. (2)The distribution pattern of suburban areas in Chengdu basically conforms to its planned urban hierarchy system.(3)Urban-oriented, suburban-interacted and rural-oriented suburban areas have their own characteristics in land use level, economic development and regional population characteristics.(4)Regional openness, government behavior, social development and geographical location are the core driving factors of spatial differentiation of suburban areas in Chengdu, and the interaction between urban and rural systems, the flow of social resources and the transformation of development models are the spatial mapping dimensions that affect its differentiation.(5)Chengdu can draw up detailed regulatory planning of suburban units to standardize and guide the overall development of suburban areas. In support of the global sustainable development goals, this study offers a methodology for precisely and impartially defining suburban areas, assisting in the implementation of integrated urban-rural development globally.

Effect of ultrasonic pretreatment on the emulsion rheological properties and interface protein structure of epigallocatechin-3-gallate and rice bran protein complex

The effect of ultrasonic pretreatment on the emulsion rheological properties and the structural characteristics of interface-adsorbed protein (IAP) and interface-unabsorbed protein (IUP) of rice bran protein and epigallocatechin-3-gallate complex (RBP-EGCG) were studied. Compared to RBP-EGCG without ultrasonic pretreatment, appropriate ultrasonic pretreatment (ultrasonic power was 425 W) enhanced the IAP trypsin sensitivity (from 3.20 to 3.73), increased the IUP surface hydrophobicity (from 12.59 to 20.87), and decreased the ζ-potential (from −24.93 mV to −36.88 mV) and particle size (from 567.30 nm to 273.13 nm) of IUP, thereby increasing the viscosity and viscoelasticity of emulsion. Compared to appropriate ultrasonic pretreatment, high-power ultrasonic pretreatment (ultrasonic power was 500 W) attenuated the IAP trypsin sensitivity, and increased the ζ-potential and particle size of IUP, thereby decreasing the viscosity and viscoelasticity of emulsion. Overall, ultrasonic pretreatment changed the EGCG-RBP emulsion viscoelasticity by regulating spatial structural characteristics and flexibility of interface protein.

MAFNet: a two-stage multiple attention fusion network for partial-to-partial point cloud registration

Abstract 3D point cloud registration is a critical technology in the fields of visual measurement and robot automation processing. In actual large-scale industrial production, the accuracy of point cloud registration directly affects the quality of automated welding processes. However, most existing methods are confronted with serious challenges such as the failure of partial-to-partial point cloud model registration when facing robot automatic processing guidance and error analysis work. Therefore, this paper proposes a novel two-stage network architecture for point cloud registration, which aims at robot pose adjustment and visual guidance in the field of automated welding by using 3D point cloud data. Specifically, we propose a neighborhood-based multi-head attention module in the coarse registration stage. The neighborhood information of each point can be aggregated through focusing on different weight coefficients of multi-head inputs. Then the spatial structure features which is used to establish the overlapping constraint of point clouds are obtained based on above neighborhood information. In the fine registration stage, we propose the similarity matching removal module based on multiple attention fusion features to explore deeper features from different aspects. By using deep fusion features to guide the similarity calculation, the interference of non-overlapping points is removed to achieve the finer registration. Eventually, we compare and analyze the proposed method with the SOTA ones through several error metrics and overlap estimation experiments based on the ModelNet40 dataset. The test results indicate that our method, relative to other mainstream techniques, achieves lower error rates and the most superior accuracy of 98.61% and recall of 98.37%. To demonstrate the generalization performance of proposed algorithm, extensive experiments on the Stanford 3D Scanning Repository, 7-Scenes and our own scanning dataset using partially overlapping point clouds individually under clean and noisy conditions show the validity and reliability of our proposed registration network.

Spatial structure and individual competition characteristics of secondary Mongolian oak mature forests in the mountainous area of eastern Liaoning Province, China.

The community structure of natural mature forests is determined by long-term forest succession, characterized by rational structure, rich biodiversity, and high ecological function. Understanding the spatial structure and formation mechanisms of mature forests is a fundamental prerequisite for forest management. We analyzed four structure parameters, including diameter structure, angular scale, size ratio, and mixture degree, as well as the Hegyi competition index, of secondary Quercus mongolica (Mongolian oak) mature forests in the mountainous area of eastern Liaoning Province. The results showed that Q. mongolica predominated the tree layer. In the sapling layer, Q. mongolica, Tilia amurensis, and Acer pictum were the dominant species. In the seedling layer, Acer pseudosieboldianum, T. amurensis, and A. amurensis dominated, with very few Q. mongolica seedlings. The overall diameter distribution of the stand showed an inverse "J" shape, while the diameter distribution of Q. mongolica, the dominant tree species, followed a normal distribution. The horizontal spatial structure of the stand was generally randomly distributed, with an average angle scale of 0.505, size ratio of 0.219, and mixture degree of 0.670 for Q. mongolica. From the perspective of spatial structure binary distribution, Q. mongolica individuals which had a random distribution exhibited greater growth advantages and higher levels of mixing, in comparison to other distribution types. Randomly distributed dominant and subdominant individuals made up nearly half individuals in the stand, and showed a high degree of mixing with surrounding trees. The stand-level individual tree competition index decreased with increasing diameter classes. When the diameter at breast height exceeded 20 cm, the competition index tended to stabilize (ΔCI<2). The competitive radius of individual Q. mongolica trees was 8 m, with intraspecific competition as the main pressure. Other species experienced competition pressure primarily from interspecific sources. Our results suggested that competition played an important role in shaping the spatial structure of secondary Q. mongolica mature forests.