Partitioning Method Research Articles

Machine Learning-Based Partition Method for Cyclic Development Mode of Submarine Soil Martials from Offshore Wind Farms

Offshore wind turbines are subjected to long-term cyclic loads, and the seabed materials surrounding the foundation are susceptible to failure, which affects the safe construction and normal operation of offshore wind turbines. The existing studies of the cyclic mechanical properties of submarine soils focus on the accumulation strain and liquefaction, and few targeted studies are conducted on the hysteresis loop under cyclic loads. Therefore, 78 representative submarine soil samples from four offshore wind farms are tested in the study, and the cyclic behaviors under different confining pressures and CSR are investigated. The experiments reveal two unique development modes and specify the critical CSR of five submarine soil martials under different testing conductions. Based on the dynamic triaxial test results, the machine learning-based partition models for cyclic development mode were established, and the discrimination accuracy of the hysteresis loop were discussed. This study found that the RF model has a better generalization ability and higher accuracy than the GBDT model in discriminating the hysteresis loop of submarine soil, the RF model has achieved a prediction accuracy of 0.96 and a recall of 0.95 on the test dataset, which provides an important theoretical basis and technical support for the design and construction of offshore wind turbines.

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.

Towards Agile and Judicious Metadata Load Balancing for Ceph File System via Matrix-based Modeling

To scale out the massive metadata access, the Ceph distributed file system (CephFS) adopts a dynamic subtree partitioning method, splitting the hierarchical namespace and distributing subtrees across multiple metadata servers. However, this method suffers from a severe imbalance problem that may result in poor performance due to its inaccurate imbalance prediction, ignorance of workload characteristics, and unnecessary/invalid migration activities. To eliminate these inefficiencies, we propose Lunule, a novel CephFS metadata load balancer, which employs an imbalance factor model for accurately determining when to trigger re-balance and tolerate unharmful imbalanced situations. Lunule further adopts a workload-aware migration planner to appropriately select subtree migration candidates. Finally, we extend Lunule to Lunule + , which models metadata accesses into matrices, and employs matrix-based formulas for more accurate load prediction and re-balance decision. Compared to baselines, Lunule achieves better load balance, increases the metadata throughput by up to 315.8%, and shortens the tail job completion time by up to 64.6% for five real-world workloads and their mixture, respectively. Besides, Lunule is capable of handling the metadata cluster expansion and the workload growth, and scales linearly on a 16-node cluster. Compared to Lunule, Lunule + achieves up to 64.96% better metadata load balance, and 13.53-86.09% higher throughput.

A method for module partition in undefined complex systems

Module partition is a cornerstone of modular design. Currently, the majority of module partition methods are devised for established systems with preliminary designs in place. However, for systems in the conceptual design phase, where the interrelations among internal components are yet to be fully undefined, such as small modular reactors, there exists a dearth of mature partition methods. In this paper, a new partition method tailored for undefined systems is proposed. Firstly, the random network is utilized to describe the mathematical model of the undefined system. Based on this, the well-known community detection method, the Louvain algorithm, is introduced for module partition for the undefined systems. Furthermore, a new module partition scheme that integrates the Louvain algorithm with intelligent optimization algorithms is proposed to address the problem that the Louvain algorithm is prone to be trapped into local optima for complex-structure undefined systems. Several systems, including simple-structure systems and undefined complex-structure systems, are constructed for validation, and results demonstrate that the newly proposed module partition method significantly enhances modularity and achieves superior partition outcomes for complex systems compared to other existing methods.

NACA0012 airfoil at Reynolds numbers between 50,000 and 140,000 — Part 1: Steady freestream

NACA0012 airfoil at Reynolds numbers between 50,000 and 140,000 — Part 1: Steady freestream

Intelligent partitioning method for free-form grid structure based on generative adversarial networks

Intelligent partitioning method for free-form grid structure based on generative adversarial networks

Milnor fiber consistency via flatness

AbstractWe describe a new algebro‐geometric perspective on the study of the Milnor fibration and, as a first step toward putting it into practice, prove powerful criteria for a deformation of a holomorphic function germ to admit a stratification on its domain partially satisfying the Thom condition and, more generally, to respect the Milnor fibration of the original germ in an appropriate sense. As corollaries, we obtain a method of partitioning the space of homogeneous polynomials of a fixed degree into finitely many locally closed subsets such that the fiber diffeomorphism type of the Milnor fibration is constant along each subset and a criterion under which deformations of a function with critical locus a complete intersection will be well‐behaved.

Cluster Partitioning Method for High-PV-Penetration Distribution Network Based on mGA-PSO Algorithm

To tackle the issues of scattered distributed photovoltaic access points and unbalanced cluster partitioning scales, an iterative clustering partitioning method is proposed, a which integrates micro-evolution genetic algorithm and particle swarm optimization (mGA-PSO). In this method, the complementary aspects of active and reactive power are quantified as key indicators, and node membership is incorporated to construct a comprehensive metric for the partitioning of a distributed PV cluster. Additionally, to improve the optimal search performance of high-penetration photovoltaic cluster partitioning, an enhanced learning-based modification factor is introduced in the genetic algorithm population selection, and a search and transfer mechanism based on historical population information is incorporated into the particle swarm algorithm. This enhances the particle swarm optimization capability with individual intelligent feedback. Experimental tests on the IEEE 34-node and IEEE 110-node systems demonstrate that the proposed method outperforms GA and PSO approaches in cluster partitioning, improving the convergence speed of the algorithm while avoiding local optima.

Unveiling the Nutrient Signatures in Corn (Zea mays L.) Grains: A Pivotal Indicator of Yield Potential

The composition simplex (N, P, K, Ca, and Mg) of the leaf is the main score used by different approaches, like the Diagnosis and Recommendation Integrated System and Compositional Nutrient Diagnosis, to study nutrient interactions and balance in plant leaves. However, the application and validation of these concepts to grain composition remains unexplored. Contrary to foliar analysis’s early intervention for nutrient deficiency detection and correction, applying this approach to seeds assesses diverse cultivars’ potential, enabling anticipation of their adaptation to climate conditions and informed selection for future crops. In the present study, a collected database of more than 924 scores, including the grain yield (kg ha−1) and the nutrient composition (mg kg−1) of different corn varieties, is used to develop a novel nutrient-based diagnostic approach to identify reliable markers of nutrient imbalance. A ‘nutrient signature’ model is proposed based on the impact of the environmental conditions on the nutrient indices and composition (N, P, K, Ca, and Mg) of the corn grains. The yield threshold used to differentiate between low- and high-yielding subpopulations is established at 12,000 kg ha−1, and the global nutrient imbalance index (GNII) of 2.2 is determined using the chi-square distribution function and validated by the Cate–Nelson partitioning method, which correlated yield data distribution with the GNII. Therefore, the nutrient compositions were classified into highly balanced (GNII ≤ 1.6), balanced (1.6 < GNII ≤ 2.2), and imbalanced (GNII > 2.2). In addition, we found that the Xgboost model’s predictive accuracy for the GNII is significantly affected by soil pH, organic matter, and rainfall. These results pave the way for adapted agricultural practices by providing insights into the nutrient dynamics of corn grains under varying environmental conditions.

Quantifying variation of non-point source pollution and its impact factors: A study of Nansi Lake Basin.

Agricultural non-point source (NPS) pollution directly affects the quality of soil and water, ecological balance and human health, and is a key challenge to achieve sustainable environmental development and efficient resource management. Taking the Nansi Lake Basin (NLB) as the study area, this study explores the main sources of agricultural NPS pollution and its influencing factors, aiming to provide scientific basis for the management of water resources in the basin. Current studies usually use the runoff pollution partitioning method to estimate agricultural NPS pollution loads in runoff, but the accuracy of the analyses is limited by the incompleteness of water quality monitoring data, especially the lack of complete runoff records in some years. To compensate for this deficiency, this study simulated the river runoff based on the Long-Term Hydrological Impact Assessment (L-THIA) model, and applied the simulation results to the quantitative calculation of agricultural NPS pollution loads after verifying the model reliability through accuracy calibration. Based on L-THIA model, the spatial and temporal distribution data of agricultural NPS pollution in the basin from 2010 to 2020 were obtained, the distribution characteristics of chemical oxygen demand (COD) and ammonia nitrogen (NH3-N) were quantitatively assessed, and the impacts of natural and socio-economic factors on them were analyzed. A regression model was developed to simulate future agricultural NPS pollution through multiple regression analysis. The results showed that the total agricultural NPS pollution in the NLB showed an increasing trend during the study period. In particular, among the socio-economic factors, COD and NH3-N were significantly correlated with fertilizer application, pesticide use, rural employment and total population. Among the natural factors, topographic index, watershed area and gully density were positively correlated with pollutants, while slope and soil organic matter were negatively correlated. The results of this study raise awareness of the contribution of influencing factors and allow researchers and planners to focus on the most important NPS pollution sources and influencing factors. The study provides an important reference for the prevention and control of agricultural NPS pollution in the NLB, which is of great practical importance.

EVCS-DAS: Evolving Visual Cryptography Schemes for Dynamic Access Structures

A systematic investigation of evolving visual cryptography scheme (EVCS) is carried out in this article. The evolving scheme, denoted as \((k,\infty)\) , differs from the \((k,n)\) threshold in that it permits an arbitrary and perhaps unlimited number of participants. More importantly, the access structure can be updated dynamically by adding new users. First of all, a preliminary implementation strategy for the \((2,\infty)\) EVCS is introduced. Then, by employing the \((2,2)\) VCS recursively with the \((2,\infty)\) EVCS, a \((k,\infty)\) EVCS is created. In order to enhance the performance, an improved scheme is constructed based on the multi-secret VCS (MVCS) and a series of EVCS schemes with thresholds of \((1,\infty)\) , \(\cdots\) , \((k-1,\infty)\) . Moreover, Boolean XOR operation is adopted for secret recovery to further improve the visual quality. To facilitate the XOR decryption, a novel access structure partition algorithm is presented. Additionally, the proposed partition method can successfully solve the security issue in existing multi-secret XOR-based VCS (MXVCS). By integrating the more secure MXVCS into the improved scheme, XOR decryption is provided. The two proposed methods are shown to be effective and advantageous through extensive experiments and comparisons.

Five-axis hybrid manufacturing with DED and milling for complex multi-branched metallic parts

ABSTRACT Hybrid manufacturing (HM), by combining metal additive manufacturing (AM) and subtractive machining (SM) on a single platform, allows the manufacturing of complex metallic parts with high dimensional accuracy and surface quality, which originally were impossible to manufacture solely by either AM or SM. The key to HM is to plan an optimal AM/SM sequence with a minimal number of alternations while satisfying both the machinable and printable constraints. In this paper, a continuous five-axis HM sequence planning method is proposed for LP-DED in which all the building direction, nozzle axis and cutter axis are allowed to change continuously. Firstly, a guide curve-based model partition method is proposed to decompose a given multi-branched model into a set of blocks, each of which is adaptively sliced into a series of high-quality non-parallel planar layers, conforming to the AM manufacturing characteristics. Secondly, a beam-guided downward expansion algorithm is proposed to search for the optimal HM segments by evaluating each layer’s five-axis printability and machinability, each of which will be first printed and then machined subsequently. Physical experiments of five test models are performed on a homebuilt five-axis hybrid manufacturing center, whose results verify the feasibility and capabilities of the proposed methodology.

Multi-center decomposition of molecular densities: A numerical perspective.

In this study, we analyze various Iterative Stockholder Analysis (ISA) methods for molecular density partitioning, focusing on the numerical performance of the recently proposed Linear approximation of Iterative Stockholder Analysis (LISA) model [Benda et al., J. Chem. Phys. 156, 164107 (2022)]. We first provide a systematic derivation of various iterative solvers to find the unique LISA solution. In a subsequent systematic numerical study, we evaluate their performance on 48 organic and inorganic, neutral and charged molecules and also compare LISA to two other well-known ISA variants: the Gaussian iterative stockholder analysis and Minimum Basis Iterative Stockholder analysis (MBIS). The study reveals that LISA-family methods can offer a numerically more efficient approach with better accuracy compared to the two comparative methods. Moreover, the well-known issue with the MBIS method, where atomic charges obtained for negatively charged molecules are anomalously negative, is not observed in LISA-family methods. Despite the fact that LISA occasionally exhibits elevated entropy as a consequence of the absence of more diffuse basis functions, this issue can be readily mitigated by incorporating additional or integrating supplementary basis functions within the LISA framework. This research provides the foundation for future studies on the efficiency and chemical accuracy of molecular density partitioning schemes.

Automatic Annotation Method for Music Post-Production Based on Hybrid Boltzmann Machine and Pitch Features

In order to improve the accuracy of music post-production annotation and the score of music post-production automatic annotation, this paper proposes a music post-production automatic annotation method based on a hybrid Boltzmann machine (HBM) and pitch features. First, obtain the post-production music label dataset, and obtain the song theme label dataset and song sentiment label dataset through statistical analysis. Second, the dataset is partitioned using a dataset partitioning method and stored in MP3 format. Then, the discrete Fourier transform (DFT) is used to process the frames of the audio to obtain the spectrum, and a mixed Boltzmann machine is used to perform low-pass filtering and downsampling on each subband to obtain the amplitude envelope of the audio on the time axis. The first five peaks obtained are added together to obtain a beat histogram, which enables the extraction of pitch features for later production music. Finally, the music sequence is modeled using the BRNN network structure, and the attention weights of post-production music sequence features are calculated. The Mel score of the song is preprocessed, and the prediction probability of the song’s music labels is obtained through music classification methods. The configuration information of the labels is read. The labels are filtered based on the threshold in the label configuration information, the final labels are stored in the database, and an automatic annotation method is implemented for music post-production. The results show that the annotation accuracy of our method can reach 99.97%, and the annotation score can reach 0.98. Our method has stronger robustness.

The Potential Impact of the Three Gorges Reservoir on Regional Extreme Precipitation—A Sensitivity Study

Understanding the potential impact of the Three Gorges Reservoir (TGR) on regional extreme precipitation and its mechanisms is critical for the safe operation of the reservoir and the efficient management of regional water resources. This study uses the regional climate model RegCM4 to conduct a double-nested simulation experiment (50 km to 10 km) from 1989 to 2012, evaluated against the CN5.1 observation dataset. Sensitivity experiments with three different lake area ratios (0%, 20% and 100%) were performed using the sub-grid partitioning method in the Community Land Model Version 4.5 to analyze the spatiotemporal distribution, intensity, and frequency of precipitation under varying TGR water areas. The results show that with a 20% lake area ratio, precipitation slightly decreases, but the impact on extreme precipitation indices is not statistically significant. However, with a 100% lake area ratio, significant decreases in both total and extreme precipitation indices occur. The reduction is primarily driven by the formation of anomalous mountain-valley circulation between the TGR and surrounding mountains, which leads to atmospheric subsidence and reduced convective activity. These findings indicate that while the TGR has a negligible impact on extreme precipitation under its current configuration, the exaggerated sensitivity experiments reveal potential mechanisms and localized effects. This research enhances the understanding of the TGR’s influence on regional extreme precipitation and provides valuable insights for water resource management and reservoir operation.

Extraction Optimization and Bioactivity of Polysaccharides from Ganoderma leucocontextum Spores.

Background: Oxidative stress is associated with the occurrence and progress of aging. Natural polysaccharides have attracted considerable attention in the field of antioxidants and anti-aging products due to their superior biological activity and low toxicity. Ganoderma leucocontextum is primarily found in the Tibetan plateau region and is classified as a subspecies of Ganoderma. Known as the famous white Ganoderma, it is a precious food and medicine that has potent biological activity, including antitumor, hypoglycemic, and immune regulation. Since available resources are limited, there are few studies on the spore of Ganoderma leucocontextum. Methods: In this work, a polysaccharide (named GLSP) was extracted from the spore of Ganoderma leucocontextum using a fast, simple, efficient, and environmentally friendly extraction process: the three-phase partitioning (TPP) method. Results: The extraction condition was optimized under the Box-Behnken design (BBD): ratio of the solute to the solvent, 1:21.126 (w/v); (NH4)2SO4 concentration, 30% (w/v); ratio of the slurry to tert-butanol, 1:1.945 (v/v); and shaking temperature, 54.136 °C. Furthermore, a polysaccharide termed GLSP-A1 was purified from GLSP by column chromatography. The basic physicochemical properties were analyzed by molecular weight, Fourier transform infrared spectroscopy, monosaccharide composition, and scanning electron microscopy. Conclusions: GLSP-A1 down-regulated the expression of the pro-inflammation cytokines interleukin-6 and interleukin-1β, indicating favorable in vitro anti-inflammatory properties. In vivo, the effect of GLSP-A1 on aging was examined using the Caenorhabditis model. The results showed that GLSP-A1 reduced reactive oxygen species levels and lipofuscin accumulation. In general, these findings improve our understanding of the chemical content and bioactivity of a polysaccharide from Ganoderma leucocontextum spore and highlight the possibility of GLSP-A1 being utilized in dietary supplements for its anti-aging properties.

Index growth not imputable to topology

We employ partitioning methods, in the spirit of Montiel–Ros but here recast for general actions of compact Lie groups, to prove effective lower bounds on the Morse index of certain families of closed minimal hypersurfaces in the round four-dimensional sphere, and of free boundary minimal hypersurfaces in the Euclidean four-dimensional ball. Our analysis reveals, in particular, phenomena of linear index growth for sequences of minimal hypersurfaces of fixed topological type, in strong contrast to the three-dimensional scenario.

A flash flood susceptibility prediction and partitioning method based on GeoDetector and random forest

A flash flood susceptibility prediction and partitioning method based on GeoDetector and random forest

Exploring Intrinsic Bond Properties with the Fukui Matrix from Conceptual Density Matrix Functional Theory.

We extend the traditional conceptual density functional theory (CDFT) to conceptual density matrix functional theory (CDMFT) by replacing the external potential v(r) by the one-electron integral hrs in the energy functional. This approach provides a new path for investigating intrinsic bond properties such as bond reactivity. The derivation of the Fukui matrix, i.e., derivative of the density matrix P with respect to the number of electrons N, is elucidated, and the result is illustrated in a case study on H2O. The matrix is shown to play a crucial role in quantifying changes of bond strength for electron removal or addition processes via the bond order derivative . Using the Mayer bond order and different atoms-in-molecules partitioning methods, we show that as a first-order response quantity, the bond order derivative agrees well with the finite difference bond order changes. The bond order derivative (bond Fukui function) is a bond reactivity descriptor. We demonstrate this by predicting the regioselectivity of a classical electrophilic addition reaction (the bromination of alkenes) and predicting the initial electron-driven bond cleavage in mass spectrometry. Specifically, the bond order derivative captures all of the major signals from the experimental mass spectra for a series of small molecules with a variety of functional groups.

Direct mixed-mode I&II partitioning in beam-like geometries using Digital Image Correlation and the decomposed [formula omitted]-integral

Direct mixed-mode I&II partitioning in beam-like geometries using Digital Image Correlation and the decomposed [formula omitted]-integral