The raw data contains two types of data, one is the frequency domain distribution of RAS DO influence coefficients of aeration, water flow and feeding; the other one is the joint calculation data of the influence coefficients' RNN regression sub model. For each influence coefficient, include 4 txt files, the files were real part and imaginary part of frequency domain distribution with condition or without condtion respectively. The joint calculation data is a 'mat' file of MatLab, 16 variables are included: AR_IMAG: indicates the imag part of aeration RNN regression calculator sub-model output; AR_REAL: indicates the real part of aeration RNN regression calculator sub-model output; FD_IMAG:indicates the imag part of feeding RNN regression calculator sub-model output; FD_REAL:indicates the real part of feeding RNN regression calculator sub-model output; WF_IMAG:indicates the imag part of water flow RNN regression calculator sub-model output; WF_REAL:indicates the real part of water flow RNN regression calculator sub-model output; DCM_simulation1: indicates the DCM time domain simulation sequences data; SIMU_AMPLITUDE_AR: The frequency distribution of aeration RNN regression calculator sub-model output. SIMU_AMPLITUDE_FD:The frequency distribution of feeding RNN regression calculator sub-model output. SIMU_AMPLITUDE_WF:The frequency distribution of water flow RNN regression calculator sub-model output. T: The time axis. monitoring_data: The monitoring data of RAS tank DO. target: The name of the mat file, for save the variables more conveniently.

Joint frequency distribution of risk factors for the different combinations of ANC contact and place of delivery (PD), EMDHS 2019.

where A and a are segregating alleles at a putative QTL, T and t are alleles at the test marker locus. Allele frequency of A is q, allele frequency of T is p. Q and R are conditional probabilities of marker allele T given QTL allele A and a respectively, which are formulated as and where D is the coefficient of linkage disequilibrium between the marker and QTL. μ, d and h are population mean, additive and dominance genic effects at the QTL.

This is the replication data for the journal article "TiFA: An Efficient and Robust LSPIV Algorithm Based on Joint Distribution Analysis". The original article developed a new LSPIV algorithm, Time Frequency Analysis (TiFA), to improve computational efficiency and enhance the accuracy of velocity measurements in traditional LSPIV. TiFA’s performance was assessed by comparison with other image velocimetry algorithms, including traditional LSPIV, Ensemble Correlation (EC), Large-Scale Particle Tracking Velocimetry (LSPTV), and Seeding Density Index (SDI). The evaluations were conducted using an experimental hydraulic model (an indoor physical model) and two field cases: the Bradano River case and the Arrow River case. This dataset contains the following data: (1) The video footage and frames collected from the indoor physical model; (2) The Matlab code of TiFA and an example of using TiFA to process velocity data from the indoor physical model. Please note that the video footage collected from the indoor physical model is original and included in this open-access dataset. Datasets from the field cases (i.e., the Bradano River case and the Arrow River case) are sourced from a third-party study (Perks et al., 2020; see the citation below) and are NOT included this open-access dataset. Readers can access the data of the two filed cases from: Perks, M. T., Dal Sasso, S. F., Hauet, A., Jamieson, E., Le Coz, J., Pearce, S., Peña-Haro, S., Pizarro, A., Strelnikova, D., Tauro, F., Bomhof, J., Grimaldi, S., Goulet, A., Hortobágyi, B., Jodeau, M., Käfer, S., Ljubičić, R., Maddock, I., Mayr, P., & Paulus, G. (2020). Towards harmonisation of image velocimetry techniques for river surface velocity observations. Earth System Science Data, 12(3), 1545–1559. https://doi.org/10.5194/essd-12-1545-2020

Age-frequency distributions of dead skeletal material on the landscape or seabed—information on the time that has elapsed since the death of individuals—provide decadal- to millennial-scale perspectives both on the history of production and on the processes that lead to skeletal disintegration and burial. So far, however, models quantifying the dynamics of skeletal loss have assumed that skeletal production is constant during time-averaged accumulation. Here, to improve inferences in conservation paleobiology and historical ecology, we evaluate the joint effects of temporally variable production and skeletal loss on postmortem age-frequency distributions (AFDs) to determine how to detect fluctuations in production over the recent past from AFDs. We show that, relative to the true timing of past production pulses, the modes of AFDs will be shifted to younger age cohorts, causing the true age of past pulses to be underestimated. This shift in the apparent timing of a past pulse in production will be stronger where loss rates are high and/or the rate of decline in production is slow; also, a single pulse coupled with a declining loss rate can, under some circumstances, generate a bimodal distribution. We apply these models to death assemblages of the bivalve Nuculana taphria from the Southern California continental shelf, finding that: (1) an onshore-offshore gradient in time averaging is dominated by a gradient in the timing of production, reflecting the tracking of shallow-water habitats under a sea-level rise, rather than by a gradient in disintegration and sequestration rates, which remain constant with water depth; and (2) loss-corrected model-based estimates of the timing of past production are in good agreement with likely past changes in local production based on an independent sea-level curve.

Distribution map (raster format: geotiff) of Larix decidua, computed using the NFIs - EFDAC EForest European dataset of species presence/absence. The distribution is estimated by means of statistical interpolation (constrained spatial multi-frequency analysis, C-SMFA) Available years: 2000.

Joint frequencies of players in spacing group as defined by k-means cluster (rows) versus time in days delta between 1st and 95th match (columns).

Distribution map (raster format: geotiff) of Fraxinus ornus, computed using the NFIs - EFDAC EForest European dataset of species presence/absence. The distribution is estimated by means of statistical interpolation (constrained spatial multi-frequency analysis, C-SMFA)

Available years: 2000.

The maps are available in the European Forest Data Center (EFDAC). The specific goal of EFDAC is to become a focal point for policy relevant forest data and information by hosting and pointing to relevant forest information as well as providing web-based tools for accessing information located in EFDAC.

Evolutionary theory has produced two conflicting paradigms for the adaptation of a polygenic trait. While population genetics views adaptation as a sequence of selective sweeps at single loci underlying the trait, quantitative genetics posits a collective response, where phenotypic adaptation results from subtle allele frequency shifts at many loci. Yet, a synthesis of these views is largely missing and the population genetic factors that favor each scenario are not well understood. Here, we study the architecture of adaptation of a binary polygenic trait (such as resistance) with negative epistasis among the loci of its basis. The genetic structure of this trait allows for a full range of potential architectures of adaptation, ranging from sweeps to small frequency shifts. By combining computer simulations and a newly devised analytical framework based on Yule branching processes, we gain a detailed understanding of the adaptation dynamics for this trait. Our key analytical result is an expression for the joint distribution of mutant alleles at the end of the adaptive phase. This distribution characterizes the polygenic pattern of adaptation at the underlying genotype when phenotypic adaptation has been accomplished. We find that a single compound parameter, the population-scaled background mutation rate $\Theta_{bg}$, explains the main differences among these patterns. For a focal locus, $\Theta_{bg}$ measures the mutation rate at all redundant loci in its genetic background that offer alternative ways for adaptation. For adaptation starting from mutation-selection-drift balance, we observe different patterns in three parameter regions. Adaptation proceeds by sweeps for small $\Theta_{bg} \lesssim 0.1$, while small polygenic allele frequency shifts require large $\Theta_{bg} \gtrsim 100$. In the large intermediate regime, we observe a heterogeneous pattern of partial sweeps at several interacting loci.

The Site Frequency Spectrum (SFS) and the heterozygosity of allelic variants are among the most important summary statistics for population genetic analysis of diploid organisms. We discuss the generalization of these statistics to populations of autopolyploid organisms in terms of the joint Site Frequency/Dosage Spectrum and its expected value for autopolyploid populations that follow the standard neutral model. Based on these results, we present estimators of nucleotide variability from High-Throughput Sequencing (HTS) data of autopolyploids and discuss potential issues related to sequencing errors and variant calling. We use these estimators to generalize Tajima's D and other SFS-based neutrality tests to HTS data from autopolyploid organisms. Finally, we discuss how these approaches fail when the number of individuals is small. In fact, in autopolyploids there are many possible deviations from the Hardy–Weinberg equilibrium, each reflected in a different shape of the individual dosage distribution. The SFS from small samples is often dominated by the shape of these deviations of the dosage distribution from its Hardy–Weinberg expectations.

Steel sandwich beams and panels with prismatic cores offer a promising alternative to traditional structures in various industries because of their excellent mechanical characteristics. This research explores performance gains by optimizing the core of the beams using a topology optimization (TO) framework to improve stress distribution and natural frequency. The beams include structural joints to the surrounding structures, which has not been investigated before for these types of structures. To address computational demands, accelerated linear finite element (FE) solvers and eigensolvers are employed, specifically adapted for density-based TO to enhance efficiency and maintain accuracy. The inexact recycled implicitly restarted Lanczos method is proposed, providing a novel approach to efficiently solving eigenvalue problems by recycling eigenvectors and relaxing convergence tolerances, significantly speeding up the process. The topology optimized beams are compared to conventional prismatic sandwich beams (X-core, Y-core, corrugated-core, and web-core), which are optimized using a global evolutionary algorithm. Limits on design variables are used to ensure ease of production. The results show that topology optimized beams outperform conventional beams by up to 44% in terms of stress and 18% in terms of frequency, at higher mass levels. Although they resemble conventional beams, optimized core topologies with joints highlight additional improvements and underscore the importance of joint design in optimization. Accelerated solvers reduce computational time by up to 99%, enabling TO to generate Pareto fronts comparable to global sizing optimization. Certain limitations, such as reduced performance at volume fractions below 0.2, indicate potential areas for further study.

Frequency distribution of research subjects based on risk factors of MMP3 gene expression rs 679620 (n = 90).

Unhealthy air quality conditions can strongly affect long-term human health and wellbeing, yet many air quality data products focus on near real-time alerts or short-term forecasts. Understanding the full state of air quality also requires examining the longer term frequency and intensity of poor air quality at ground level, and how it might change over time. We present a new modeling framework to compute climate-adjusted estimates of air quality hazards for the contiguous United States (CONUS) at 10 km horizontal resolution. The framework blends results from statistical, machine-learning, and climate-chemistry models—including a bias-adjusted version of the EPA Community Multiscale Air Quality Model (CMAQ) time series as described in (Wilson et al., 2022)—for ground-level ozone, anthropogenic fine particulate matter (PM2.5), and wildfire smoke PM2.5 into consistent estimates of days exceeding the “unhealthy for sensitive groups” (orange colored) classification on the EPA Air Quality Index for 2023 and 2053. We find that joint PM2.5 and ozone orange+ days range from 1 day to 41 days across CONUS, with a median value of 2 days, across all years. Considering all properties across CONUS, we find that 63.5% percent are exposed to at least one orange or greater day in 2023, growing to 72.1% in 2053. For a 7-day threshold, 3.8% and 5.7% of properties are exposed in 2023 and 2053, respectively. Our results also support the identification of which parts of the country are most likely to be impacted by additional climate-related air quality risks. With growing evidence that even low levels of air pollution are harmful, these results are an important step forward in empowering individuals to understand their air quality risks both now and into the future.

Distribution of frequency of PARP-1 SNPs in brain tumor patients and controls.

Absolute frequencies and percentages of radiographic findings in the ventrodorsal and frog-leg views of the left and right pelvic limbs.