Multiple Partitions Research Articles

Interval-valued picture fuzzy decision-making framework with partitioned maclaurin symmetric mean aggregation operators

Interval-valued picture fuzzy (IVPF) set is an extension of the picture fuzzy set theory used to represent uncertainty and vagueness in the processes of decision-making. This study focuses on exploring the interrelationships among multiple IVPFSs and criteria partitions. We investigate the IVPF partitioned Maclaurin symmetric mean operator and the weighted IVPF partitioned Maclaurin symmetric mean operator and discuss their respective properties. Subsequently, we identify certain special cases of these operators based on IVPF sets. Furthermore, we deploy a multi-criteria decision-making procedure utilizing the suggested IVPF partition operators. Through a numerical example, we demonstrate the practicality and validity of the presented approach. Finally, a thorough comparison with existing approaches is conducted to elucidate the superiority of the proposed method.

L-FNNG: Accelerating Large-Scale KNN Graph Construction on CPU-FPGA Heterogeneous Platform

Due to the high complexity of constructing exact k -nearest neighbor graphs, approximate construction has become a popular research topic. The NN-Descent algorithm is one of the representative in-memory algorithms. To effectively handle large datasets, existing state-of-the-art solutions combine the divide-and-conquer approach and the NN-Descent algorithm, where large datasets are divided into multiple partitions, and a subgraph is constructed for each partition before all the subgraphs are merged, reducing the memory pressure significantly. However, such solutions fail to address inefficiencies in large-scale k -nearest neighbor graph construction. In this paper, we propose L-FNNG, a novel solution for accelerating large-scale k -nearest neighbor graph construction on CPU-FPGA heterogeneous platform. The CPU is responsible for dividing data and determining the order of partition processing, while the FPGA executes all construction tasks to utilize the acceleration capability fully. To accelerate the execution of construction tasks, we design an efficient FPGA accelerator, which includes the Block-based Scheduling (BS) and Useless Computation Aborting (UCA) techniques to address the problems of memory access and computation in the NN-Descent algorithm. We also propose an efficient scheduling strategy that includes a KD-tree-based data partitioning method and a hierarchical processing method to address scheduling inefficiency. We evaluate L-FNNG on a Xilinx Alveo U280 board hosted by a 64-core Xeon server. On multiple large-scale datasets, L-FNNG achieves, on average, 2.3× construction speedup over the state-of-the-art GPU-based solution.

Sectoral electricity micro-spatial load forecasting based on partitional clustering technique

<p>Load demand forecasting is crucial in energy supply planning due to economic progress and territorial expansion, where land utilization transforms dynamically. An accurate sectoral load prediction can preclude the loss of beneficial opportunities arising from excessive load demand or excessive investment at a low-growth juncture. However, the particular area in this sectoral approach is still relatively large, rendering it incapable of precisely projecting load at minor points (micro-spatial). This study has proposed a micro-spatial load prediction strategy that categorizes identified areas into smaller grids or districts. This procedure includes clustering similar sites together for improved accuracy. K-Means is one of the partitional clustering approaches, a clustering algorithm utilizing object-based centroid-based partitioning approaches. The algorithm determines a cluster's centroid or centre as the average point for the cluster. This technique is advantageous as it can process extensive data efficiently and is appropriate for circular data. This technique can divide the data into multiple partitions, ensuring that each object belongs to precisely one cluster. Subsequently, mathematical modelling is used to predict the load of each cluster, which can then be utilized to more accurately evaluate the positions and sizes of prospective substations, transmission, and distribution facilities.</p>

Detectability of hierarchical communities in networks.

We study the problem of recovering a planted hierarchy of partitions in a network. The detectability of a single planted partition has previously been analyzed in detail and a phase transition has been identified below which the partition cannot be detected. Here we show that, in the hierarchical setting, there exist additional phases in which the presence of multiple consistent partitions can either help or hinder detection. Accordingly, the detectability limit for nonhierarchical partitions typically provides insufficient information about the detectability of the complete hierarchical structure, as we highlight with several constructive examples.

Active Clustering Ensemble With Self-Paced Learning.

A clustering ensemble provides an elegant framework to learn a consensus result from multiple prespecified clustering partitions. Though conventional clustering ensemble methods achieve promising performance in various applications, we observe that they may usually be misled by some unreliable instances due to the absence of labels. To tackle this issue, we propose a novel active clustering ensemble method, which selects the uncertain or unreliable data for querying the annotations in the process of the ensemble. To fulfill this idea, we seamlessly integrate the active clustering ensemble method into a self-paced learning framework, leading to a novel self-paced active clustering ensemble (SPACE) method. The proposed SPACE can jointly select unreliable data to label via automatically evaluating their difficulty and applying easy data to ensemble the clusterings. In this way, these two tasks can be boosted by each other, with the aim to achieve better clustering performance. The experimental results on benchmark datasets demonstrate the significant effectiveness of our method. The codes of this article are released in https://Doctor-Nobody.github.io/codes/space.zip.

Using a Mathematical Model to Evaluate the Efficiency of Memory Allocation Algorithm

One of the key research areas in operating systems is memory allocation and process management by the operating system. Memory allocation is the process of allocating blocks of memory to different executing processes in order to improve overall system performance. This paper analyses the efficiency of memory allocation algorithms (first fit, best fit and worst fit) in the multiple partition and contiguous memory allocation scheme. A Mathematical model was used on process sizes in terms of percentage internal fragmentation (IF%), percentage external fragmentation (EF%), and percentage total utilization (TU%), this was done to compare the performance of the memory allocation algorithm. Results from the analysis show that the best fit made more efficient use of the available memory space than that of first-fit and worst fit.

Contrastive Disentanglement for Quantitative Ultrasound Muscle Atrophy Evaluation.

Muscleatrophy reduces the quality of life and increases morbidity and mortality from other diseases. The development of non-invasive muscle atrophy evaluation method is of great practical value. The lack of gold standard for pathological grading usually allows only the duration of weightlessness as a criterion for the degree of atrophy. However, the adaptive reductive remodeling of muscle physiology and structure shows a trend of nonlinear changes in time. Consequently, using weightlessness time as a benchmark for the degree of atrophy is inaccurate. This paper proposes a new ultrasound imaging-based method for quantifying muscle atrophy that utilizes weakly supervised information between multiple data partitions with controlled variance components, overcoming the limitations of using the weightlessness time as a criterion. We introduce a group-supervised contrastive disentanglement network (GCDNet) to disentangle the individual variances, muscle growth and atrophy components of ultrasound images, and quantify the degree of atrophy using the disentangled atrophy component. The feasibility of GCDNet is validated by the separability, independence, and representativeness of the disentangled components. To simplify the application of GCDNet, a muscle atrophy scoring network requiring no reference images is developed by distilling the GCDNet's knowledge of muscle atrophy quantization. The strength of the proposed methodology allows us, for the first time to our knowledge, to study the muscle growth attribute during hind-limb unloading and the spatial distribution of muscle atrophy.

The role of the environment and connectivity on multiple facets of local fish diversity in tropical headwater streams

Abstract The identification of multiple facets of biodiversity (e.g., phylogenetic and functional) can help to understand how evolutionary and ecological mechanisms shape the assembly of biological communities. Here, we employed multiple regressions and variance partitions to investigate the effects of environmental conditions, connectivity and space on aspects of the functional and phylogenetic facets of fish fauna in tropical headwater streams of the Xingu River basin, in Eastern Amazonia. We found that the functional and phylogenetic diversities of fish communities are related to local environmental conditions and stream connectivity in the drainage network, with marked differences occurring between the rainy and dry periods. Furthermore, we discovered that functional uniqueness was influenced mainly by environmental factors, whereas phylogenetic uniqueness had a more complex relationship with the influence of local environmental factors and connectivity. Our findings indicate that the fish assemblages of small headwater streams of the Xingu River basin share a close phylogenetic history. This is probably because of the climatic and environmental stability experienced by Amazonian ichthyofauna over evolutionary time. Our findings emphasise the role of niche‐based processes in shaping the unique functional composition of fish assemblages in headwater streams, and highlight the importance of environmental heterogeneity in maintaining the ecological integrity of these freshwater ecosystems.

Model-based multifacet clustering with high-dimensional omics applications.

High-dimensional omics data often contain intricate and multifaceted information, resulting in the coexistence of multiple plausible sample partitions based on different subsets of selected features. Conventional clustering methods typically yield only one clustering solution, limiting their capacity to fully capture all facets of cluster structures in high-dimensional data. To address this challenge, we propose a model-based multifacet clustering (MFClust) method based on a mixture of Gaussian mixture models, where the former mixture achieves facet assignment for gene features and the latter mixture determines cluster assignment of samples. We demonstrate superior facet and cluster assignment accuracy of MFClust through simulation studies. The proposed method is applied to three transcriptomic applications from postmortem brain and lung disease studies. The result captures multifacet clustering structures associated with critical clinical variables and provides intriguing biological insights for further hypothesis generation and discovery.

Drivers of human–tiger conflict risk and potential mitigation approaches

AbstractHuman–wildlife conflict has become a significant challenge for conservationists, particularly in areas where endangered species, such as large carnivores, are recovering. If we fail to keep a balance between the interests of humans and wildlife, the human–wildlife conflict can have adverse outcomes. However, the drivers of human–wildlife conflict, and how to mitigate conflict, are often poorly understood. In this study, we aimed to explore the possible causes for and potential mitigating approaches to human–tiger conflict risks through spatiotemporal niche partitioning. Based on data from the reports of Amur tiger (Panthera tigris altaica) preying on cattle and camera trap detection data from 2014 to 2019 in Hunchun, Northeast China, we predicted Amur tiger occurrence and created risk maps of human–tiger potential encounters. We found that Amur tiger occurrence was positively driven by prey distribution and negatively by the distribution of pastures used for domestic cattle grazing. Livestock was increasingly predated in areas with limited preferred prey, that is, wild pig (Sus scrofa) and sika (Cervus nippon), and in closer proximity to cattle‐grazing land. On the basis of our models, we divided areas utilized by human and Amur tigers into low‐, medium‐, and high‐risk areas across multiple spatiotemporal scales. We suppose that multiple spatiotemporal scale niche partitioning management might effectively reduce the risk of human–tiger encounters, prompt harmonized coexistence between humans and tigers, and provide new solutions to other areas experiencing human–wildlife conflicts.

NLOCL: Noise-Labeled Online Continual Learning

Continual learning (CL) from infinite data streams has become a challenge for neural network models in real-world scenarios. Catastrophic forgetting of previous knowledge occurs in this learning setting, and existing supervised CL methods rely excessively on accurately labeled samples. However, the real-world data labels are usually misled by noise, which influences the CL agents and aggravates forgetting. To address this problem, we propose a method named noise-labeled online continual learning (NLOCL), which implements the online CL model with noise-labeled data streams. NLOCL uses an empirical replay strategy to retain crucial examples, separates data streams by small-loss criteria, and includes semi-supervised fine-tuning for labeled and unlabeled samples. Besides, NLOCL combines small loss with class diversity measures and eliminates online memory partitioning. Furthermore, we optimized the experience replay stage to enhance the model performance by retaining significant clean-labeled examples and carefully selecting suitable samples. In the experiment, we designed noise-labeled data streams by injecting noisy labels into multiple datasets and partitioning tasks to simulate infinite data streams realistically. The experimental results demonstrate the superior performance and robust learning capabilities of our proposed method.

Ensemble clustering via synchronized relabelling

Ensemble clustering is an important problem in unsupervised learning that aims at aggregating multiple noisy partitions into a unique clustering solution. It can be formulated in terms of relabelling and voting, where relabelling refers to the task of finding optimal permutations that bring coherence among labels in input partitions. In this paper we propose a novel solution to the relabelling problem based on permutation synchronization. By effectively circumventing the need for a reference clustering, our method achieves superior performance than previous work under varying assumptions and scenarios, demonstrating its capability to handle diverse and complex datasets.

How to be indifferent

AbstractAccording to the principle of indifference, when a set of possibilities is evidentially symmetric for you – when your evidence no more supports any one of the possibilities over any other – you're required to distribute your credences uniformly among them. Despite its intuitive appeal, the principle of indifference is often thought to be unsustainable due to the problem of multiple partitions: Depending on how a set of possibilities is divided, it seems that sometimes, applying indifference reasoning can require you to assign incompatible credences to equivalent possibilities. This paper defends the principle of indifference from the problem of multiple partitions by offering two guides for how to respond. The first is for permissivists about rationality, and is modeled on permissivists' arguments for the claim that a body of evidence sometimes does not uniquely determine a fully rational credence function. The second is for impermissivists about rationality, and is modeled on impermissivists' arguments for the claim that a body of evidence does always uniquely determine a fully rational credence function. What appears to be a decisive objection against the principle of indifference is in fact an instance of a general challenge taking different forms familiar to both permissivists and impermissivists.

Polyamide 6 microplastics as carriers led to changes in the fate of bisphenol A and dibutyl phthalate in drinking water distribution systems: The role of adsorption and interfacial partitioning

Microplastics (MPs) co-exist with plastic additives and other emerging pollutants in the drinking water distribution systems (DWDSs). Due to their strong adsorption capacity, MPs may influence the occurrence of additives in DWDSs. The article investigated the occurrence of typical additives bisphenol A (BPA) and dibutyl phthalate (DBP) in DWDSs under the influence of polyamide 6 (PA6) MPs and further discussed the partitioning of BPA/DBP on PA6s, filling a research gap regarding the impact of adsorption between contaminants on their occurrence within DWDSs. In this study, adsorption experiments of BPA/DBP with PA6s and pipe scales were conducted and their interaction mechanisms were investigated. Competitive adsorption experiments of BPA/DBP were also carried out with site energy distribution theory (SEDT) calculations. The results demonstrated that PA6s might contribute to the accumulation of BPA/DBP on pipe scales. The adsorption efficiencies of BPA/DBP with both PA6s and pipe scales were 26.47 and 2.61 times higher than those with only pipe scales. It was noteworthy that BPA had a synergistic effect on the adsorption of DBP on PA6s, resulting in a 26.47 % increase in DBP adsorption. The article provides valuable insights for the compounding effect of different types of additives in water quality monitoring and evaluation.

Partitioning genetic structure of a subterranean rodent at multiple spatial scales: accounting for isolation by barriers, distance, and environment

ContextUnderstanding genetic structure at multiple spatial scales and identifying drivers of genetic isolation are important for developing comprehensive conservation plans including for grassland conservation efforts. However, few studies account for multiple genetic isolation processes nor partition genetic variance among these processes.ObjectivesWe assess key processes that can create spatial genetic patterns including isolation by barrier (IBB), isolation by distance (IBD), and isolation by environment (IBE) for a widespread pocket gopher species (Geomys bursarius) and a spatially restricted subspecies (Geomys bursarius illinoensis). We further partition genetic variation to each isolating effect and identify genetic variation that was shared between processes.MethodsWe used seven microsatellites to determine spatial genetic clustering and identify environmental factors impacting genetic similarities. Then, we used redundancy analysis to partition variance explained by IBB, IBD, and IBE.ResultsMajor rivers including the Mississippi River acted as barriers and explained the most genetic variation across the species. In contrast, IBD explained the most genetic variation for G. b. illinoensis. Gophers had genetic associations to soil sand percent and soil color, but IBE uniquely explained a small amount of genetic structure for G. bursarius, with additional variation shared with other isolating processes.ConclusionsGopher genetic structure resulted from barriers, distance, and environmental factors at the species range as well as for a subspecies’ region, but the relative amount of genetic variance assigned to unique isolating processes differed between scales. Delineation of conservation units should consider major rivers as natural boundaries, and finer-scale management should identify and protect areas close to source populations with similar soil friability. Our study exemplifies how analyzing gene flow at rangewide and regional scales can aid managers in developing localized strategies that fit within broader conservation units.

Numerical Study of Heat Transfer in a Partitioned Cavity Containing a Porous Medium

Cavities separated by multiple vertical partitions and filled with a porous medium present a remarkable thermal insulation quality, offering potential solutions in various engineering fields. The aim of this study is to analyze the impact of the presence of a porous medium on heat transfer through a partitioned cavity. We have developed a numerical model based on the Navier-Stokes and heat transfer equations, solved using Ansys Fluent software. We examine the evolution of the Nusselt number (convection and radiation) as a function of the position of the porous medium inside the cavity, as well as physical properties such as emissivity, wall conductivity and Rayleigh number. Current lines and isotherms are obtained from this numerical model. Nusselt numbers for both convection and radiation are calculated, taking into account the position of the porous medium in the system, as well as the effect of varying physical parameters on heat transfer. It has been observed that the presence of the porous medium leads to a reduction in the rate of heat transfer within the cavity. The further the porous medium is from the hot wall, the more pronounced this reduction. In addition, radiative transfer has a downward influence on convective transfer. Furthermore, the convective transfer rate decreases with increasing emissivity. As far as conductivity is concerned, transfer rates (convective and radiative) initially increase until a maximum Nusselt number is reached, after which they gradually decrease with a further increase in conductivity. Nusselt numbers (convection and radiation) increase as the Rayleigh number increases.

Source apportionment of soil PTE in a northern industrial county using PMF model: Partitioning strategies and uncertainty analysis

Positive matrix factorization (PMF) has commonly been applied for source apportionment of potentially toxic elements (PTE) in agricultural soil, however, spatial heterogeneity of PTE significantly undermines the accuracy and reliability of PMF results. In this study, a representative industrial-agricultural hub in North China (Xuanhua district, Zhangjiakou City) was selected as the research subject, multiple partition processing (PP) strategies and uncertainty analyses were integrated to advance the PMF modeling and associated algorithm mechanisms were comparatively discussed. Specifically, we adopted three methods to split the research area into several subzones according to industrial density (PP-1), population density (PP-2), and the ecological risk index (PP-3) respectively, to rectify the spatial bias phenomenon of PTE concentrations and to achieve a more interpretable result. Our results indicated that the obvious enrichment of Cd, Pb, and Zn was found in the agricultural soil, with Hg and Cd accounted for 83.49% of the overall potential ecological risk. Combining proper PP with PMF can significantly improve the modelling accuracy. Uncertainty analysis showed that interval ratios of tracer species (Cd, Pb, Hg, and Zn) calculated by PP-3 were consistently lower than that of PP-1 and PP-2, indicating that PP-3 coupled PMF can afford the optimal modeling results. It suggested that natural sources, fertilizers and pesticides, atmosphere deposition, mining, and smelting were recognized as the major contributor for the soil PTE contamination. The contribution of anthropogenic activities, specifically fertilizers and pesticides, and atmosphere deposition, increased by 1.64% and 5.91% compared to PMF results. These findings demonstrate that integration of proper partitioning processing into PMF can effectively improve the accuracy of the model even at the case of soil PTE contamination with high heterogeneity, offering support to subsequently implement directional control strategies.

Design and hydraulic study of double‐partition dividing‐wall column

AbstractDividing‐wall column (DWC) provides tremendous advantages in terms of energy savings and capital costs for three‐component and even multi‐component separations. With increasing industrial application of three‐component DWCs, DWCs for separation of four‐component have received considerable research attention. The aim of this work is to investigate the design of the two‐staggered wall region and gas–liquid two‐phase flow behavior on the tray in a double‐partition DWC (DPDWC). The Aspen Plus was applied to simulate the steady state to obtain the gas–liquid‐phase flow rate in each region to serve as the basis for the tray design. In different task regions, the gas‐ and liquid‐phase flow varied in some ways. The drag models have variability for the study of fluid flow, and the Tomiyama model is more appropriate. Moreover, high‐velocity gradients and recirculating phenomenon can be discovered owing to the existence of multiple partitions in DPDWC.

Warm places, warm years, and warm seasons increase parasitizing of moose by winter ticks

AbstractObserved links between parasites, such as ticks, and climate change have aroused concern for human health, wildlife population dynamics, and broader ecosystem effects. The one‐host life history of the winter tick (Dermacentor albipictus) links each annual cohort to environmental conditions during three specific time periods when they are predictably vulnerable: spring detachment from hosts, summer larval stage, and fall questing for hosts. We used mixed‐effects generalized linear models to investigate the drivers of tick loads carried by moose (Alces alces) relative to these time periods and across 750 moose, 10 years, and 16 study areas in the western United States. We tested for the effects of biotic factors (moose density, shared winter range, vegetation, migratory behavior) and weather conditions (temperature, snow, humidity) during each seasonal period when ticks are vulnerable and off‐host. We found that warm climatic regions, warm seasonal periods across multiple partitions of the annual tick life cycle, and warm years relative to long‐term averages each contributed to increased tick loads. We also found important effects of snow and other biotic factors such as host density and vegetation. Tick loads in the western United States were, on average, lower than those where tick‐related die‐offs in moose populations have occurred recently, but loads carried by some individuals may be sufficient to cause mortality. Lastly, we found interannual variation in tick loads to be most correlated with spring snowpack, suggesting this environmental component may have the highest potential to induce change in tick load dynamics in the immediate future of this region.

Consensus Clustering With Co-Association Matrix Optimization.

Consensus clustering can derive a more promising and robust clustering result by integrating multiple partitions strategically. However, there are several limitations in the existing approaches: 1) most of the methods compute the ensemble-information matrix heuristically and lack of sufficient optimization; 2) the information from the original dataset is rarely considered; and 3) the noise in both label space and feature space is ignored. To address these issues, we proposed a novel consensus clustering method with co-association matrix optimization (CC-CMO), which aims at improving the co-association matrix by taking abundant information from both label space and feature space into consideration. In label space, CC-CMO derives a weighted partition matrix capturing the intercluster correlation and further designs a least squares regression (LSR) model to explore the global structure of data. In feature space, CC-CMO minimizes the reconstruction error with doubly stochastic normalization in the projective subspace to eliminate noise features as well as learn the local affinity of data. To improve the co-association matrix by jointly considering the subspace representation, global structure, and local affinity of data, we explicitly propose a unified optimization framework and design an alternating optimization algorithm for the optimal co-association matrix. Extensive experiments on a variety of real-world datasets demonstrate the superior performance of CC-CMO to the state-of-the-art consensus clustering approaches.