Space Partitioning Research Articles

Kernel contextual fuzzy rule model based on conditional input space partitioning driven by data reconstruction in autoencoder and randomization-based neural networks

Kernel contextual fuzzy rule model based on conditional input space partitioning driven by data reconstruction in autoencoder and randomization-based neural networks

Apple Trajectory Prediction in Orchards: A YOLOv8-EK-IPF Approach

To address the challenge of accurate apple harvesting by orchard robots, which is hindered by dynamic changes in apple position due to wind interference and branch swaying, this study proposes an optimized prediction algorithm based on an integration of the extended Kalman filter (EKF) and an improved particle filter (IPF), built upon initial apple detection and recognition using YOLOv8. The algorithm first employs spatial partitioning according to the cyclical motion patterns of apples to constrain the prediction results. Subsequently, it optimizes the rationality of particle weights within the particle filter (PF) and reduces its computational resource consumption by implementing historical position weighting and an adaptive particle number strategy. Finally, an adaptive error correction mechanism dynamically adjusts the respective weights of the EKF and IPF components, continuously enhancing the algorithm’s prediction accuracy. Experimental results demonstrate that, compared to the classic unscented Kalman filter (UKF) and unscented particle filter (UPF), the proposed EK-IPF algorithm reduces the mean absolute error (MAE) by 22.25% and 10.89%, respectively, and the root mean square error (RMSE) by 23.70% and 13.25%, respectively, indicating a significant improvement in overall prediction accuracy. This research provides technical support for dynamic apple trajectory prediction in orchard environments.

Partial proper orthogonal decomposition: Numerical applications and comparisons

Isolating localized phenomena in complex, multi-scale, and multi-physics flow fields, such as those encountered in scramjet engines or combustion enhancement studies, remains a significant challenge due to the dominance of global modes and the limitations of traditional decomposition methods. To address this, we propose a partial proper orthogonal decomposition (partial-POD) method based on spatial partitioning, which selectively decomposes submatrices of regions of interest to extract localized features. Theoretical validation using a synthetic dataset confirms the method's ability to precisely separate time-evolution coefficients and spatial distributions of individual modes. Practical applications in fluid dynamics, including multiscale cylindrical bypassing and dynamic injection boundary conditions, demonstrate its effectiveness in revealing localized flow characteristics and providing physically meaningful modal decompositions. Additionally, the method serves as a computationally efficient low-order approximation of standard POD, significantly reducing computational costs while maintaining accuracy. These results underscore the potential of the partial-POD method as a practical tool for analyzing localized phenomena in complex flow fields, offering insight for engineering and research applications.

TORC1 autonomously controls its spatial partitioning via the Rag GTPase tether Tco89.

The eukaryotic target of rapamycin complex 1 (TORC1) kinase is a homeostatic regulator of growth, integrating nutritional cues at the endolysosomal compartment. Amino acids activate mammalian TORC1 (mTORC1) through the Rag GTPases that recruit it to lysosomes via a short domain within the mTORC1 subunit Raptor. Intriguingly, this "Raptor claw" domain is absent in Kog1, the Raptor ortholog in yeast. Instead, as we show here, yeast utilizes the fungal-specific Tco89 to tether TORC1 to active Rag GTPases. This interaction enables TORC1 to precisely calibrate the activity of the S6K1-related effector kinase Sch9 in response to amino acid availability. TORC1 stabilizes Tco89 by phosphorylation, and its inactivation causes swift Tco89 proteolysis, provoking a redistribution of TORC1 from the vacuole to signaling endosomes and its spatial separation from Sch9. Thus, TORC1 not only operates in spatially distinct subcellular pools but also controls its own quantitative distribution between these pools to economize energy resources under fluctuating nutrient conditions.

A Distributed Neural Hybrid System Learning Framework in Modeling Complex Dynamical Systems.

In this article, a distributed neural network modeling framework including a novel neural hybrid system model is proposed for enhancing the scalability of neural network models in modeling dynamical systems. First, high-dimensional training data samples will be mapped to a low-dimensional feature space through the principal component analysis (PCA) featuring process. Following that, the feature space is bisected into multiple partitions based on the variation of the Shannon entropy under the maximum entropy (ME) bisecting process. The behavior of subsystems in the prespecified state space partitions will then be approximated using a group of shallow neural networks (SNNs) known as extreme learning machines (ELMs), and then it can further simplify the model by merging the redundant lattices based on their training error performance. The proposed modeling framework can handle high-dimensional dynamical system modeling problems with the advantages of reducing model complexity and improving model performance in training and verification. To demonstrate the effectiveness of the proposed modeling framework, examples of modeling the LASA dataset and an industrial robot are presented.

Product centred Dirichlet processes for Bayesian multiview clustering

Abstract While there is an immense literature on Bayesian methods for clustering, the multiview case has received little attention. This problem focuses on obtaining distinct but statistically dependent clusterings in a common set of entities for different data types. For example, clustering patients into subgroups with subgroup membership varying according to the domain of the patient variables. A challenge is how to model the across-view dependence between the partitions of patients into subgroups. The complexities of the partition space make standard methods to model dependence, such as correlation, infeasible. In this article, we propose CLustering with Independence Centring (CLIC), a clustering prior that uses a single parameter to explicitly model dependence between clusterings across views. CLIC is induced by the product centred Dirichlet process, a novel hierarchical prior that bridges between independent and equivalent partitions. We show appealing theoretic properties, provide a finite approximation and prove its accuracy, present a marginal Gibbs sampler for posterior computation, and derive closed-form expressions for the marginal and joint partition distributions for the CLIC model. On synthetic data and in an application to epidemiology, CLIC accurately characterizes view-specific partitions while providing inference on the dependence level.

Exploring Spatial and Temporal Patterns in the Size‐At‐Age of Pacific Halibut in the Gulf of Alaska

ABSTRACTLarge changes in Pacific halibut (Hippoglossus stenolepis) size‐at‐age across decades have affected available fishery yield and management decisions. Characterizing mechanisms driving variability in size‐at‐age of adult Pacific halibut is challenging due to uncertainty associated with spatial and temporal partitioning of the phenomenon. We explored spatial and temporal changes in size‐at‐age structure of Pacific halibut in the Gulf of Alaska during 1998–2021, and the relationship between spatial patterns in size‐at‐age and diet using δ13C and δ15N analysis of muscle tissues collected in 2018. Pacific halibut size‐at‐age was spatially and temporally structured across the study area, with distinct regions of persistent anomalously high or low relative size‐at‐age. Further, trophic feeding level varied spatially and diet was related to size‐at‐age. Our results provide important context for investigations of specific mechanisms influencing Pacific halibut size‐at‐age and future research on Pacific halibut ecology. We recommend revisiting fundamental aspects of Pacific halibut biology, sampling methodology, and management practices from this spatiotemporal perspective.

Organ structure and bacterial microbiogeography in a reproductive organ of the Hawaiian bobtail squid reveal dimensions of a defensive symbiosis.

Many plants and animals house symbiotic microorganisms in specialized tissues or organs. Here, we used multidimensional in situ imaging techniques to illuminate how host organ structure and bacterial microbiogeography contribute to the symbiotic function of an organ in the Hawaiian bobtail squid, Euprymna scolopes. Along with the well-studied light organ, female E. scolopes harbor a community of bacteria in the accessory nidamental gland (ANG). The ANG is a dense network of epithelium-lined tubules, some of which are dominated by a single bacterial taxon. These bacteria are deposited into squid eggs, where they defend the developing embryos from harmful biofouling. This study used a combination of imaging techniques to visualize different dimensions of the ANG and its bacterial communities. Imaging entire organs with light sheet microscopy revealed that the ANG is a composite tissue of individual, non-intersecting tubules that each harbor their own bacterial population. The organ is bisected, with tubules converging toward two points at the posterior end. At these points, tubules empty into a space where bacteria can mix with squid jelly to be deposited onto eggs. Observations of the symbiotic community correlated bacterial taxa with cell morphology and revealed that tubule populations varied: some tubules contained populations of mixed taxa, whereas others contained only one bacterial genus. Together, these data shed light on how bacterial populations interact within the ANG and how the host uses physical structure to maintain and employ a symbiotic bacterial population in a defensive context.IMPORTANCESequence-based microbiome studies have revealed much about how hosts interact with communities of symbiotic microbiota but often lack a spatial understanding of how microbes relate to each other and the host in which they reside. This study uses a combination of microscopy techniques to reveal how the structure of a symbiotic organ in the female bobtail squid, Euprymna scolopes, houses diverse, beneficial bacterial populations and deploys them for egg defense. These findings suggest that spatial partitioning may be key to harboring a diverse population of antimicrobial-producing bacteria and establishing a foundation for further understanding how host structures mediate symbiotic interactions.

A method for mining empirical route networks adaptable to complex geographical maritime areas

Given that existing methods for mining empirical route networks can only cover major routes and cannot adapt to complex geographical maritime areas such as coastlines and areas near islands and reefs, this paper proposes a method capable of adapting to these complex geographical maritime regions. First, considering the navigational constraints imposed by the geographical environment, an adaptive quadtree operator is introduced to generate a multi-scale navigable space that aligns with the geographical environment. This ensures effective full coverage of complex maritime areas such as coastlines and areas near islands and reefs. Then, based on the association characteristics between quadtree spatial subdivision coding and latitude and longitude, massive trajectory data is efficiently matched to the navigable space. The trajectory data is reorganized and simplified using quadtree code indexing. Next, collision detection is performed between the trajectory data and the navigable space to filter out effective tracks. Finally, using the single-center density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm, dense cores of each navigable space are extracted. The filtered track data is subjected to segmented clustering within the navigable space to mine multi-scale empirical maritime route networks. Experimental results show that: (1) the proposed method can mine route networks in complex geographical maritime areas such as coastlines and islands, overcoming the limitations of existing empirical networks that can only handle major routes; (2) by fully utilizing the navigable space, the proposed method shortens the average voyage distance of the main routes by 4.96% compared to existing methods.

Spatial partitioning of floral volatiles provides a "chemosensory roadmap" for bumblebee pollinators.

Spatial partitioning of floral volatiles provides a "chemosensory roadmap" for bumblebee pollinators.

Enhanced Grid Based K-Medoids Clustering Technique for Customer Segmentation

Among the existing clustering algorithms, the K-Medoids algorithm is widely used due to its robustness and effectiveness. However, its performance can be affected by the selection of initial cluster centers and sensitivity to noise. To address these issues, this paper proposes an improved K-Medoids clustering algorithm. A density-based noise removal technique is introduced through Grid to filter out outliers and obtain regional density. Grid-based clustering with K-Medoids improves efficiency by reducing the number of distance calculations through space partitioning. It is robust to noise and outliers since KMedoids selects actual data points as representatives. The approach reduces computational complexity by clustering grid cells instead of all data points. Additionally, the influence of density estimation errors on centre selection is reduced through granularity-based adjustments, minimizing manual intervention in the peak density algorithm. This approach enhances clustering accuracy and stability

Patterns and Mechanisms of Niche Partitioning Between Related Parasitoids (Hymenoptera) Sharing the Same Host Species.

Related species of parasitoid Hymenoptera often coexist on a certain host, but many details of interactions between these organisms remain unclear. The present review summarizes the main existing concepts and facts and suggests principal patterns and mechanisms that allow for the coexistence of several members of a particular parasitoid genus at the expense of the same host. Although the successful introduction of exotic parasitic wasps into the existing ecosystems often leads to the competitive displacement of related parasitoids, mere spatial and/or temporal niche partitioning between these insects is also possible. Nevertheless, many cases of coexistence of related wasp species on the same host defy simple explanations since they apparently result from complex interactions between the host and its parasitoids. The main characteristics of the oviposition process, i.e., egg volume, fecundity, and duration of the egg-laying period, are likely to correlate with other basic features of life-history strategies in parasitoid Hymenoptera. Specialist parasitic wasps often aggregate over the host patches, whereas generalists can be randomly distributed, thus reducing the degree of interspecific competition among parasitoids. However, some of the coexisting parasitic wasps, usually the weakest competitors, must also have access to enemy-free space to survive.

Unraveling HIV-1 transmission patterns through molecular surveillance in a hotspot in Yunnan Province, China

Honghe Prefecture, Yunnan Province, China, is a key area affected by HIV/AIDS. Given the high number of infected individuals, understanding HIV transmission in this region is crucial to controlling the epidemic in Yunnan Province. A molecular epidemiology study with 1004 newly diagnosed HIV-1 cases in 2022 was conducted to characterize transmission and guide targeted interventions. Of the 833 samples genotyped, nine HIV-1 genotypes were identified, with CRF08_BC (56.7%), URFs (17.8%), CRF07_BC (13.9%) and CRF01_AE (7.1%) being the most prevalent. These genotypes were widely distributed but concentrated in the center and east of the prefecture. Molecular network analysis showed varying clustering rates among counties, with Luxi County having the highest and Honghe County the lowest. Low education levels were associated with higher clustering. Cross-county transmission patterns were revealed, with strong links between specific counties. Of the three spatial subdivisions identified by the cohesive subgroup analysis, subgroup I included eight counties running from northeast to southwest. Persons aged 50–59 years and ethnic minorities were more likely to be identified in cross-county transmission. Notably, the overall prevalent level of HIV-1 pre-treatment drug resistance was 6.2% (50/809), with higher levels in Luxi (18.2%, 8/44), Hekou (14.3%, 3/21) and Yuanyang (10.9%, 13/119). This study investigated HIV-1 transmission patterns using molecular network analysis in a hotspot of HIV transmission, and informed the application of HIV molecular surveillance in practice.

Generation of Higher-Order Poincaré Beams with Polarization States Varying Along the Propagation Direction Based on Dielectric Metasurfaces.

Vector beams (VBs) with longitudinally varying polarization states provide a new dimension for light field manipulation, and promote the advancements of related areas such as optical metrology, longitudinal depth detection, and classical and quantum communications. In this study, we propose a half-wave plate dielectric metasurface based on a spatial partitioning method, realizing the longitudinal manipulation of the polarization states of higher-order Poincaré (HOP) beams by changing the elliptical polarization state of the incident light and selecting the appropriate propagation distances. The metasurface is composed of two sub-metasurfaces, and the two sets of a-Si:H meta-atoms are uniformly arranged on concentric rings of different radii with an equal interval. The propagation and Pancharatnam-Berry phases are utilized to construct the axicon and helical phase profiles. As a result, two sub-metasurfaces, respectively, generate the first- and second-order VBs with longitudinally varying polarization states. The polarization states of generated VBs correspond to points on different meridians of nth-order HOP spheres from the south pole to the north pole. The consistency between the theoretical and simulated results demonstrates the feasibility and practicability of the proposed method. This study provides an innovative strategy to extend the modulation of light fields from two-dimensional to three-dimensional space.

Pore Space Partition Enabled by Lithium(I) Chelation of a Metal-Organic Framework for Benchmark C2H2/CO2 Separation.

Adsorptive separation of acetylene (C2H2) from carbon dioxide (CO2) offers a promising approach to purify C2H2 with low-energy footprints. However, the development of ideal adsorbents with simultaneous high C2H2 adsorption and selectivity remains a great challenge due to their very small molecular sizes and physical properties. Herein, we report a lithium(I)-chelation strategy for pore space partition (PSP) in a microporous MOF (Li+@NOTT-101-(COOH)2) to achieve simultaneous high C2H2 uptake and selectivity. The chelation model of Li+ ions within the framework was visually identified by single-crystal X-ray diffraction studies. The immobilized Li+ ions were found to have two functions: (1) partitioning large pore cages into smaller ones while maintaining high surface area and (2) providing specific binding sites to selectively take up C2H2 over CO2. The resulting Li+@NOTT-101-(COOH)2 exhibits a rare combination of a simultaneous high C2H2 capture capacity (205 cm3 g-1) and C2H2/CO2 selectivity (13) at ambient conditions, far surpassing that of NOTT-101-(COOH)2 (148 cm3 g-1 and 3.8, respectively) and most top-tier materials reported. Theoretical calculations and gas-loaded SCXRD studies reveal that the chelated Li+ ions combined with the segmented small cages can selectively bind with a large amount of C2H2 through the unique π-complexation, accounting for the improved C2H2 uptake and selectivity. Breakthrough experiments validated its excellent separation capacity for actual C2H2/CO2 mixtures, providing one of the highest C2H2 productivities of 118.9 L kg-1 (>99.5% purity) in a single adsorption-desorption cycle.

Retrieval of Soil Moisture in the Yutian Oasis, Northwest China by 3D Feature Space Based on Optical and Radar Remote Sensing Data

Soil moisture in arid areas serves as a vital indicator for assessing hydrological scarcity and ecosystem vulnerability, particularly in Northwest China (NW China), where water resource deficits critically exacerbate environmental fragility. Soil moisture retrieval through remote sensing techniques proves essential for formulating sustainable strategies to enhance local environmental management. This study presents an innovative fusion framework integrating Sentinel-2 optical data and Radarsat-2 PolSAR (Polarimetric Synthetic Aperture Radar) data to establish a three-dimensional (3D) optical–radar feature space. The feature space synergistically combines SAR backscattering coefficients (HH polarization modes), polarimetric decomposition (volume scattering components of van Zyl), and optical remote sensing indices (MSAVI and NDVI). Through systematic analysis of feature space partitioning patterns across soil moisture gradients, the Optical–Radar Soil Moisture Retrieval Index (ORSMRI) was proposed, and fitting analysis was conducted by measured soil moisture. The results confirmed consistency between ORSMRI-derived retrieved soil moisture and measured soil moisture, with ORSMRI1 attaining R2 = 0.797 (RMSE = 3.329%) and ORSMRI2 reaching R2 = 0.721 (RMSE = 3.905%). The soil moisture in the study area was retrieved by applying the proposed ORSMRI and utilizing its linear correlation with soil moisture. The distribution of soil moisture showed a trend of being higher in the south than in the north, and higher in the west than in the east. Specifically, low soil moisture is generally concentrated in the northern and southwestern parts of the oasis, while high soil moisture is primarily concentrated in the central part of the oasis.

Global or local modeling for XGBoost in geospatial studies upon simulated data and German COVID-19 infection forecasting

Methods from artificial intelligence (AI) and, in particular, machine learning and deep learning, have advanced rapidly in recent years and have been applied to multiple fields including geospatial analysis. Due to the spatial heterogeneity and the fact that conventional methods can not mine large data, geospatial studies typically model homogeneous regions locally within the entire study area. However, AI models can process large amounts of data, and, theoretically, the more diverse the train data, the more robust a well-trained model will be. In this paper, we study a typical machine learning method XGBoost, with the question: Is it better to build a single global or multiple local models for XGBoost in geospatial studies? To compare the global and local modeling, XGBoost is first studied on simulated data and then also studied to forecast daily infection cases of COVID-19 in Germany. The results indicate that if the data under different relationships between independent and dependent variables are balanced and the corresponding value ranges are similar, i.e., low spatial variation, global modeling of XGBoost is better for most cases; otherwise, local modeling of XGBoost is more stable and better, especially for the secondary data. Besides, local modeling has the potential of using parallel computing because each sub-model is trained independently, but the spatial partition of local modeling requires extra attention and can affect results.

Human neural dynamics of real-world and imagined navigation

The ability to form episodic memories and later imagine them is integral to the human experience, influencing our recollection of the past and envisioning of the future. While rodent studies suggest the medial temporal lobe, especially the hippocampus, is involved in these functions, its role in human imagination remains uncertain. In human participants, imaginations can be explicitly instructed and reported. Here we investigate hippocampal theta oscillations during real-world and imagined navigation using motion capture and intracranial electroencephalographic recordings from individuals with chronically implanted medial temporal lobe electrodes. Our results revealed intermittent theta dynamics, particularly within the hippocampus, encoding spatial information and partitioning navigational routes into linear segments during real-world navigation. During imagined navigation, theta dynamics exhibited similar patterns despite the absence of external cues. A statistical model successfully reconstructed real-world and imagined positions, providing insights into the neural mechanisms underlying human navigation and imagination, with implications for understanding memory in real-world settings.

Research on Abstraction-Based Search Space Partitioning and Solving Satisfiability Problems

Solving satisfiability problems is central to many areas of computer science, including artificial intelligence and optimization. Efficiently solving satisfiability problems requires exploring vast search spaces, where search space partitioning plays a key role in improving solving efficiency. This paper defines search spaces and their partitioning, focusing on the relationship between partitioning strategies and satisfiability problem solving. By introducing an abstraction method for partitioning the search space—distinct from traditional assignment-based approaches—the paper proposes sequential, parallel, and hybrid solving algorithms. Experimental results show that the hybrid approach, combining abstraction and assignment, significantly accelerates solving in most cases. Furthermore, a unified method for search space partitioning is presented, defining independent and complete partitions. This method offers a new direction for enhancing the efficiency of SAT problem solving and provides a foundation for future research in the field.

Spatio-temporal evolution and drivers of coupling coordination between digital infrastructure and inclusive green growth: Evidence from the Yangtze River economic belt.

Spatio-temporal evolution and drivers of coupling coordination between digital infrastructure and inclusive green growth: Evidence from the Yangtze River economic belt.