using an idea based on iterative gradient ascent. In this paper we develop a mean-shift-inspired algorithm to estimate the modes of regression functions and partition the sample points in the input space. We prove convergence of the sequences generated by the algorithm and derive the non-asymptotic rates of convergence of the estimated local modes for the underlying regression model.

The explosive growth of molecular sequence data has made it possible to estimate species divergence times under relaxed-clock models using genome-scale datasets with many gene loci. In order both to improve model realism and to best extract information about relative divergence times in the sequence data, it is important to account for the heterogeneity in the evolutionary process across genes or genomic regions. Partitioning is a commonly used approach to achieve those goals. We group sites that have similar evolutionary characteristics into the same partition and those with different characteristics into different partitions, and then use different models or different values of model parameters for different partitions to account for the among-partition heterogeneity. However, how to partition data in practical phylogenetic analysis, and in particular in relaxed-clock dating analysis, is more art than science. Here, we use computer simulation and real data analysis to study the impact of the partition scheme on divergence time estimation. The partition schemes had relatively minor effects on the accuracy of posterior time estimates when the prior assumptions were correct and the clock was not seriously violated, but showed large differences when the clock was seriously violated, when the fossil calibrations were in conflict or incorrect, or when the rate prior was mis-specified. Concatenation produced the widest posterior intervals with the least precision. Use of many partitions increased the precision, as predicted by the infinite-sites theory, but the posterior intervals might fail to include the true ages because of the conflicting fossil calibrations or mis-specified rate priors. We analyzed a dataset of 78 plastid genes from 15 plant species with serious clock violation and showed that time estimates differed significantly among partition schemes, irrespective of the rate drift model used. Multiple and precise fossil calibrations reduced the differences among partition schemes and were important to improving the precision of divergence time estimates. While the use of many partitions is an important approach to reducing the uncertainty in posterior time estimates, we do not recommend its general use for the present, given the limitations of current models of rate drift for partitioned data and the challenges of interpreting the fossil evidence to construct accurate and informative calibrations.

Hard Drive Partitioning Software Market Outlook

The global hard drive partitioning software market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach around USD 2.4 billion by 2032, growing at a compound annual growth rate (CAGR) of 7.5% during the forecast period from 2024 to 2032. The growth of this market is driven by the increasing demand for efficient data management solutions and the proliferation of digital data across various sectors.

One of the primary growth factors for the hard drive partitioning software market is the exponential increase in data generation. Businesses and individuals are generating data at an unprecedented rate due to the widespread use of digital technologies. The need for effective organization and management of this data has led to a rising demand for advanced partitioning software that can efficiently segment hard drives into multiple partitions. This segmentation allows for better data organization, enhances system performance, and facilitates easier backups and data recovery processes.

Another significant growth driver is the increasing adoption of cloud computing and virtualization technologies. As organizations move their IT infrastructure to the cloud and implement virtualized environments, the need for robust hard drive partitioning solutions becomes critical. These technologies allow for the creation of multiple virtual partitions within a single physical hard drive, optimizing storage utilization and improving system performance. The ability to easily allocate and manage storage resources in virtualized environments is a key factor contributing to the market's growth.

Additionally, the growing emphasis on data security and compliance is boosting the demand for hard drive partitioning software. With stringent data protection regulations being enforced across various industries, organizations are increasingly adopting partitioning solutions to ensure the secure and compliant management of their data. Partitioning software helps in isolating sensitive data, preventing unauthorized access, and enabling efficient encryption and decryption processes. The rising awareness about data privacy and security is expected to further propel the market's growth in the coming years.

From a regional perspective, North America holds a significant share in the hard drive partitioning software market, driven by the presence of major technology companies and a high adoption rate of advanced IT infrastructure. Europe follows closely, with increasing investments in digital transformation initiatives and stringent data protection regulations. The Asia Pacific region is anticipated to witness the highest growth rate during the forecast period, owing to rapid technological advancements, growing digitalization, and increasing adoption of cloud computing solutions in emerging economies like China and India. Latin America and the Middle East & Africa are also expected to contribute to the market's growth, driven by the expanding IT sector and increasing focus on data management solutions.

Component Analysis

The hard drive partitioning software market is segmented into software and services. Software components dominate this segment, driven by the demand for robust and efficient partitioning tools that can handle large volumes of data. These software solutions are designed to provide easy-to-use interfaces, advanced features, and seamless integration with existing IT infrastructure, making them essential for both individual and enterprise users. Software solutions come in various forms, including standalone applications, integrated tools within operating systems, and specialized software for specific tasks such as data recovery or data migration.

Services, on the other hand, encompass a range of offerings, including installation, configuration, training, and support services. As businesses seek to optimize their data management processes, the demand for professional services to assist in the implementation and maintenance of partitioning software is growing. Service providers offer expertise in customizing partitioning solutions to meet specific organizational needs, ensuring that the software functions optimally and provides maximum benefits. These services are particularly valuable for large enterprises with complex IT infrastructures and stringent data management requirements.

The integration of artificial intelligence (AI) and machine learning (ML) techniques into partitioning software is an emerging trend within the software segment. AI-driven solutions can

Simulated datasets analysed in Rota et al. study "A simple method for data partitioning based on relative evolutionary rates". AS refers to datasets simulated on an asymmetrical trees and SS to those simulated on a symmetrical tree. The datasets are in phylip format. Having 'miss' in the name of a file refers to missing 25% of the data.

This dataset was used to conduct the study with the title "Effect of Knowledge Differentiation and State Space Partitioning on Subjective Probability Estimation"

Morphological data are a fundamental source of evidence to reconstruct the Tree of Life, and Bayesian phylogenetic methods are increasingly being used for this task. Bayesian phylogenetic analyses require the use of evolutionary models, which have been intensively studied in the past few years, with significant improvements to our knowledge. Notwithstanding, a systematic evaluation of the performance of partitioned models for morphological data has never been performed. Here we evaluate the influence of partitioned models, defined by anatomical criteria, on the precision and accuracy of summary tree topologies considering the effects of model misspecification. We simulated datasets using partitioning schemes, trees, and other properties obtained from two empirical datasets, and conducted Bayesian phylogenetic analyses. Additionally, we reanalysed 32 empirical datasets for different groups of vertebrates, applying unpartitioned and partitioned models, and, as a focused study case, we reanalysed a dataset including living and fossil armadillos, testing alternative partitioning hypotheses based on functional and ontogenetic modules. We found that, in general, partitioning by anatomy has little influence on summary topologies analysed under alternative partitioning schemes with a varying number of partitions. Nevertheless, models with unlinked branch lengths, which account for heterotachy across partitions, improve topological precision at the cost of reducing accuracy. In some instances, more complex partitioning schemes, led to topological changes, as tested for armadillos, mostly associated with models with unlinked branch lengths. We compare our results with other empirical evaluations of morphological data and those from empirical and simulation studies of partitioning of molecular data, considering the adequacy of anatomical partitioning relative to alternative methods of partitioning morphological datasets.

Hycal Energy Research Laboratories Ltd. (Hycal) conducted a series of fluid phase behavior measurements to ascertain the preferential retention effect of H2S that is commonly present as a secondary component in typical acid gas injection streams. The study is part of multi-faceted research conducted under the Plains CO2 Reduction Partnership program at the Zama Acid Gas Enhanced Oil Recovery operation in northwestern Alberta.

Presentation. For more than a decade, graphs have been used to model the voting behavior taking place in parliaments. However, the methods described in the literature suffer from several limitations. The two main ones are that 1) they rely on some temporal integration of the raw data, which causes some information loss; and/or 2) they identify groups of antagonistic voters, but not the context associated with their occurrence. In this article, we propose a novel method taking advantage of multiplex signed graphs to solve both these issues. It consists in first partitioning separately each layer, before grouping these partitions by similarity. We show the interest of our approach by applying it to a European Parliament dataset. Particularly, we study the voting behavior of French and Italian MEPs on "Agriculture and Rural Development" (AGRI) during the 2012-13 legislative year.

These are the data used in the following paper:

N. Arınık, R. Figueiredo, and V. Labatut, “Multiple partitioning of multiplex signed networks: Application to European Parliament votes,” Social Networks, vol. 60, pp. 83–102, 2020. DOI: 10.1016/j.socnet.2019.02.001 ⟨hal-02082574⟩

Source code. The code source is accessible on GitHub: https://github.com/CompNet/MultiNetVotes

Citation. If you use these data our this source code, please cite the above paper.

@Article{Arinik2020, author = {Arınık, Nejat and Figueiredo, Rosa and Labatut, Vincent}, title = {Multiple Partitioning of Multiplex Signed Networks: Application to {E}uropean {P}arliament Votes}, journal = {Social Networks}, year = {2020}, volume = {60}, pages = {83-102}, doi = {10.1016/j.socnet.2019.02.001},}----------------------------------------------Details.# RAW INPUT FILESThe 'itsyourparliament' folder contains all raw input files for further data processing. This is the same raw data that can be found in our previous Figshare repository: https://doi.org/10.6084/m9.figshare.5785833The folder structure is as follows:* itsyourparliament/** domains: There are 28 domain files. Each file corresponds to a domain (such as Agriculture, Economy, etc.) and contains corresponding vote identifiers and their "itsyourparliament.eu" links.** meps: There are 870 Members of Parliament (MEP) files. Each file contains the MEP information (such as name, country, address, etc.)** votes: There are 7513 vote files. Each file contains the votes expressed by MEPs# ROLLCALL NETWORKSThis folder contains two separate zip files regarding rollcall networks:- rollcall-networks: This folder contains only the rollcall networks that are used in the article.- all-rollcall-networks: For those who are interested in other countries or domains, we make available all rollcall networks that we can extract from raw data.Note that these rollcall networks constitute the layers of the input signed multplex network, as illustrated in Figure 1 of the article. Note also that we consider three vote types in our network extraction process: FOR, AGAINST and ABSTAIN.# ROLLCALL PARTITIONSNote that MEPs who voted similarly are connected together by positive links, and are connected by negative links to MEPs that voted differently from them. MEPs who did not vote at all (ABSENT) are isolates (nodes without anyneighbor). We identify the factions of similarly voting MEPs in the graph by solving the Correlation Clustering problem (CC).The rollcall partitions correspond to voting patterns, as illustrated in Figure 1 of the article.# ROLLCALL CLUSTERINGThis folder contains the results of Steps 3 and 4 of our workflow (see Figure 1 in the article). The structure of this folder is as follows:|_ votetypes=FAA/: 'FAA' means we consider three vote types in our analysis: FOR, AGAINST and ABSTAIN.|_ F.purity-k=2-sil=SILHOUETTE_SCORE|_ clu=CLUSTER_NO/|_ network: It corresponds to the network created through the similarity network-based approach, as explained in Section 4.4 of the article.|_ partition: It corresponds to the characteristic voting pattern, as explained in Section 4.4 of the article.----------------------------------------------

Funding: this research benefited from the support of the Agorantic FR 3621, as well as the FMJH Program PGMO and from the support to this program from EDF-THALES-ORANGE-CRITEO.

Genomewide association studies have contributed immensely to our understanding of the genetic basis of complex traits. One major conclusion arising from these studies is that most traits are controlled by many loci of small effect, confirming the infinitesimal model of quantitative genetics. A popular approach to test for polygenic architecture involves so‐called “chromosome partitioning” where phenotypic variance explained by each chromosome is regressed on the size of the chromosome. First developed for humans, this has now been repeatedly used in other species, but there has been no evaluation of the suitability of this method in species that can differ in their genome characteristics such as number and size of chromosomes. Nor has the influence of sample size, heritability of the trait, effect size distribution of loci controlling the trait or the physical distribution of the causal loci in the genome been examined. Using simulated data, we show that these characteristics have major influence on the inferences of the genetic architecture of traits we can infer using chromosome partitioning analyses. In particular, small variation in chromosome size, small sample size, low heritability, a skewed effect size distribution and clustering of loci can lead to a loss of power and consequently altered inference from chromosome partitioning analyses. Future studies employing this approach need to consider and derive an appropriate null model for their study system, taking these parameters into consideration. Our simulation results can provide some guidelines on these matters, but further studies examining a broader parameter space are needed.

The dataset includes 30 minutes values of partitioned evaporation (E) and transpiration (T), T:ET ratios, and other ancillary datasets for three ET partitioning methods viz. Flux Variance Similarity (FVS) method, Transpiration Estimation Algorithm (TEA), and Underlying Water Use Efficiency (uWUE) method for three wheat sites. Three wheat sites had different grazing treatments. For example, Site 1 was Grain-only and Graze-grain wheat for the 2016-17 and 2017-18 growing seasons, respectively. Site 2 was Grain-only wheat for the 2017-18 growing season. Site 3 was Graze-grain and Graze-out wheat for the 2016-17 and 2017-18 growing seasons, respectively. The grain-only wheat system is a single purpose to produce wheat grains only. Graze-grain wheat system has a dual purpose as it serves as a pasture for grazing cattle from November to February and is used to produce wheat grains later. Graze-out wheat system is also a single purpose crop that is grazed by the cattle for the entire season to solely serve as a pasture. FVS method performed ET partitioning using the high frequency (10 Hz) data collected from Eddy Covariance Flux stations, located near the middle of each field. The high-frequency data were also processed using the EddyPro software to get good quality estimates of different fluxes at 30-minute intervals. The processed 30-min data were used by TEA and uWUE methods for ET partitioning. Ancillary hydro-meteorological variables including net radiation, air temperature, soil water content, relative humidity, and others, also have been included in this dataset. The study sites were located at the United States Department of Agriculture, Agricultural Research Service (USDA-ARS), Grazinglands Research Laboratory, El Reno, Oklahoma. All sites were rainfed. Resources in this dataset:Resource Title: FVS output and other met data and site info. File Name: FVS_output_and_other_met_data_and_site_info.xlsxResource Description: Output of FVS model along with corresponding meteorological data and site metadata.Resource Title: TEA output. File Name: TEA_output.xlsxResource Description: Out from TEA model along with site metadata.Resource Title: WUE output. File Name: uWUE_output.xlsxResource Description: Output of WUE model run along with site metadata.

When inferring phylogenies, one important decision is whether and how nucleotide substitution parameters should be shared across different subsets or partitions of the data. One sort of partitioning error occurs when heterogeneous subsets are mistakenly lumped together and treated as if they share parameter values. The opposite kind of error is mistakenly treating homogeneous subsets as if they result from distinct sets of parameters. Lumping and splitting errors are not equally bad. Lumping errors can yield parameter estimates that do not accurately reject any of the subsets that were combined whereas splitting errors yield estimates that did not benefit from sharing information across partitions. Phylogenetic partitioning decisions are often made by applying information criteria such as the Akaike Information Criterion (AIC). As with other information criteria, the AIC evaluates a model or partition scheme by combining the maximum log-likelihood value with a penalty that depends on the number of parameters being estimated. For the purpose of selecting an optimal partitioning scheme, we derive an adjustment to the AIC that we refer to as the AICP and that is motivated by the idea that splitting errors are less serious than lumping errors. We also introduce a similar adjustment to the Bayesian Information Criterion (BIC) that we refer to as the BICP. Via simulation and empirical data analysis, we contrast AIC and BIC behavior to our suggested adjustments. We discuss these results and also emphasize why we expect the probability of lumping errors with the AICP and the BICP to be relatively robust to model parameterization.

Morphological data is a fundamental source of evidence to reconstruct the Tree of Life, and Bayesian phylogenetic methods are increasingly being used for this task, along with, or instead of, traditional parsimony approaches. Bayesian phylogenetic analyses require the use of proper evolutionary models and their performance have been intensively studied in the past few years, with significant improvements to our knowledge regarding their performance. Notwithstanding, it was only recently that partitioned models for morphology received attention in studies of empirical data, but a systematic evaluation of its performances using simulations was never performed. Here we evaluate the influence of partitioned models defined by anatomical criterion in the precision and accuracy of consensus tree topologies, evaluating the possible negative effects of under and overpartitioning. For that, we analysed datasets simulated using parameters and properties of two empirical datasets, using Bayesian phylogenetic analyses in MrBayes. Additionally, we reanalysed 32 empirical datasets for diverse groups of vertebrates, applying unpartitioned and partitioned models. We found that in general, partitioning by anatomy has little to no influences in the performance of Bayesian phylogenetic methods in respect to the metrics studied here, with analyses under alternative partitioning schemes presenting very similar tree precision and accuracy. We discuss the possible reasons for the disagreement between the results obtained here and previous studies for empirical morphological data, and with empirical and simulation studies of molecular data, discussing the adequacy of anatomical partitioning relative to alternative methods to partition morphological datasets and how morphological and molecular partitioning are related.

The partitioning problem is a key problem for distributed control techniques. The problem consists in the definition of the subnetworks of a dynamical system that can be considered as individual control agents in the distributed control approach. Despite its relevance and the different approaches proposed in the literature, no generalized technique to perform the partitioning of a network of dynamical systems is present yet. In this article, we introduce a general approach to partitioning for distributed control. This approach is composed by an algorithmic part selecting elementary subnetworks, and by an integer program, which aggregates the elementary components according to a global index. We empirically evaluated our approach on a distributed predictive control problem in the context of power systems, obtaining promising performances in terms of reduction of computation speed and resource cost, while retaining a good level of performance.

Ranges or subsets are presented in parentheses. Abbreviations: dist. = distance(s); no. = number; prop. = proportion; seq. = sequence; sp. = species; tot. = total; var. = variation. “Combined” refers to data generated in this study combined with collected GenBank/Bold data.

In this study, we combined two actual evapotranspiration datasets (ET), one obtained from a root zone water balance model and another from an energy balance model, to partition annual ET into green (rainfall-based) and blue (surface/groundwater) water sources. Time series maps of green water ET (GWET) and blue water ET (BWET) are produced for the conterminous United States (CONUS) over 2001–2015.

The global toilet partitions market, valued at $2194.5 million in 2025, is projected to experience steady growth, driven by increasing construction activity across residential, commercial, and industrial sectors. A Compound Annual Growth Rate (CAGR) of 4.4% is anticipated from 2025 to 2033, reflecting consistent demand for hygienic and durable partition solutions. The market is segmented by application (residential, commercial, industrial) and type (metals, non-metals). The commercial segment is likely the largest, fueled by ongoing renovations and new builds in offices, shopping malls, and public spaces. Growth in the residential sector is anticipated to be driven by increasing disposable incomes and rising demand for improved bathroom aesthetics and functionality. The increasing adoption of sustainable and eco-friendly materials within the non-metals segment is expected to contribute to market growth. Factors such as stringent building codes and regulations regarding hygiene and accessibility are also supporting market expansion. Competition is robust, with established players like Bobrick, Scranton Products, and Bradley Corporation alongside regional and smaller manufacturers. The North American market is expected to hold a significant share, followed by Europe and Asia-Pacific, reflecting regional variations in construction activity and economic growth. The forecast period will witness continued growth, albeit at a potentially moderated pace compared to previous years. Factors such as fluctuating raw material prices and potential economic downturns could influence the market trajectory. The rising adoption of advanced materials with improved durability and hygiene features will likely shape future market trends. Furthermore, increased emphasis on designing inclusive spaces will fuel demand for toilet partitions that meet accessibility standards. Technological advancements, like the incorporation of smart features and improved manufacturing processes, are poised to positively impact market dynamics. The market will likely see continued consolidation as larger players seek to expand their market share through mergers, acquisitions, and geographical expansion. Specific regional growth will depend on various factors, including government policies, infrastructure development, and economic conditions within each area.

Data from a laboratory study undertaken at the U.S. Geological Survey to investigate solid/water partitioning of per- and polyfluoroalkyl substances (PFAS) in New Hampshire soils and biosolids are presented here. Soils and biosolids used for the experiments were collected using PFAS-free sampling equipment, air dried, gently homogenized, and sieved (soils only). Soil samples were collected from locations with known PFAS contamination (n = 5) and nearby sites with similar soil characteristics but low expected PFAS concentrations (n = 4). Finished biosolids were collected directly from facilities at the final stage of processing and before distribution. Air-dried soils and biosolids were then used for a series of batch and column experiments to determine water/solid distribution coefficient (Kd) values. This study investigated the impact of pH, ionic strength, adsorption versus desorption, soil/biosolid type, experimental setup (batch versus column), and influence of sodium azide on Kd values. All batch and column experiments were run for 10 days as determined by a 16-day kinetics test. The dataset presented here includes concentration of PFAS, concentration of PFAS post total oxidizable precursor assay (TOPA), pH, moisture content, total organic carbon concentrations, aluminum concentrations, iron concentrations, sodium concentrations, cation exchange capacity, anion exchange capacity, grain size, and protein concentrations for the unprocessed soil and biosolids collected from the site (soils) or facility (biosolids). These are denoted as "Environmental - Biosolid" or "Environmental - Soil" samples in the data release. The dataset also includes the solid and water results (PFAS, TOPA, pH, specific conductivity, dissolved organic carbon, major anions, and metals) from the batch and column experiments, along with the calculated Kd values. Calculated Kd values are presented for every PFAS compound with detections in the solid and water phases and, with caution, can be used to help constrain estimates for PFAS mobility in the New Hampshire environment.

The data set is collected to evaluate if two parasitoids (Spathius galinae and Tetrastichus planipennisi), introduced for biocontrol of the invasive emerald ash borer (EAB), Agrilus planipennis, into North America have established niche-partitioning, co-existing populations following their sequential or simultaneous field releases to 12 hard-wood forests located in Midwest and Northeast regions of the United States. Ash trees of various sizes (large, pole-size and saplings) were debarked meter by meter in early spring of 2019 (Michigan sites) or fall of 2019 (Northeast states: Connecticut, Massachusetts and New York). Detailed data collection procedures can be found in the associated publication in Biological Control. Resources in this dataset:Resource Title: Niche partitioning and coexistence of parasitoids of the same feeding guild introduced for biological control of an invasive forest pest - Michigan data. File Name: Michigan 2019-EAB Parasitoid Niche Partition-Raw.csvResource Description: Michigan DatasetResource Software Recommended: JMP,url: https://www.JMP.com Resource Title: Niche partitioning and coexistence of parasitoids of the same feeding guild introduced for biological control of an invasive forest pest - Northeast states data. File Name: NE Dataset 2019-EAB Parasitoid Niche Partition-Raw.csvResource Description: Northeast States Data setResource Title: Niche partitioning and coexistence of parasitoids of the same feeding guild introduced for biological control of an invasive forest pest - Data Dictionary. File Name: Data Dictionary for Parasitoid niche partitioning study from Biological Control.docxResource Description: Data dictionary

Relationships among representatives of the five major Hawaiian Drosophila species groups were examined using data from eight different gene regions. A simultaneous analysis of these data resulted in a single most-parsimonious tree that (1) places the adiastola picture-winged subgroup as sister taxon to the other picture-winged subgroups, (2) unites the modified-tarsus species group with flies from the Antopocerus species group, and (3) places the white-tip scutellum species group as the most basal taxon. Because of the different gene sources used in this study, numerous process partitions can be erected within this data set. We examined the incongruence among these various partitions and the ramifications of these data for the taxonomic consensus, prior agreement, and simultaneous analysis approaches to phylogenetic reconstruction. Separate analyses and taxonomic consensus appear to be inadequate methods for dealing with the partitions in this study. Although detection of incongruence i...

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. The GO Partition Database was designed to feature ontology partitions with GO terms of similar specificity. The GO partitions comprise varying numbers of nodes and present relevant information theoretic statistics, so researchers can choose to analyze datasets at arbitrary levels of specificity. The GO Partition Database, featuring GO partition sets for functional analysis of genes from human and ten other commonly-studied organisms with a total of 131,972 genes.