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Several approaches to estimating the productivity function for a Hawkes point process with variable productivity are discussed, improved upon, and compared in terms of their root-mean-squared error and computational efficiency for various data sizes, and for binned as well as unbinned data. We find that for unbinned data, a regularized version of the analytic maximum likelihood estimator proposed by Schoenberg is the most accurate but is computationally burdensome. The unregularized version of the estimator is faster to compute but has lower accuracy, though both estimators outperform empirical or binned least squares estimators in terms of root-mean-squared error, especially when the mean productivity is 0.2 or greater. For binned data, binned least squares estimates are highly efficient both in terms of computation time and root-mean-squared error. An extension to estimating transmission time density is discussed, and an application to estimating the productivity of Covid-19 in the United States as a function of time from January 2020 to July 2022 is provided. [ABSTRACT FROM AUTHOR]

Background: The Stroop task was used to investigate differences in cognitive function between Long COVID (LC), Myalgic Encephalomyelitis / Chronic Fatigue Syndrome (ME/CFS) and healthy control subjects. Methods: Subjects viewed four color words or neutral (XXXX) stimuli with the same (congruent) or different color ink (incongruent). Cognitive conflict was inferred from response times for pairings of prestimuli and subsequent stimuli. Overall effects were assessed by univariate analysis with time courses determined for binned response times. Results: LC and ME/CFS had significantly longer response times than controls indicating cognitive dysfunction. Initial response times were ranked LC > ME > HC, and decreased according to power functions. At the end of the task (900s), times were ranked LC = ME > HC. Response times were significantly slower for stimuli following an incongruent prestimulus. Time series for Stroop effect, facilitation, interference, surprise index and practice power law parameters were generally similar in LC, ME/CFS and HC suggesting comparable patterns for recruitment of cognitive resources. The prestimulus data were analyzed and generated positive Stroop and interference effects that were distinct from stimulus effects. Conclusion: LC and ME/CFS have global slowing of response times that cannot be overcome by practice suggesting impaired communications between network nodes during problem solving. Analysis of matched prestimulus – stimulus effects adds a new dimension for understanding cognitive conflict. Brief Summary: Cognitive dysfunction in Long COVID and ME/CFS was demonstrated using the Stroop task which found global slowing of response times and limitations of practice effects. [ABSTRACT FROM AUTHOR]

Many methods for seismic risk assessment rely on the selection of a seismic intensity measure (IM) and the development of models of the seismic demand conditional on the IM. The individual importance of these two features to accurately assess seismic performance is well known. In contrast, this study aims to evaluate the impact that the combined selection of IM and the demand model has on risk estimates. Using a hypothetical seismic source model and a non‐stationary stochastic ground‐motion model, we present risk estimates for a mid‐rise steel structure for 15 different IMs and five demand models derived by cloud analysis (four based on regression and a fifth based on an empirical binning approach). The impact of these choices is investigated through a novel method of model performance evaluation using a benchmark solution obtained via the unconditional approach (i.e., directly estimating demand exceedance frequencies from simulated ground motion time histories). The obtained results are also compared against traditional IM performance metrics, for example, efficiency and sufficiency. Finally, we demonstrate how risk estimate inaccuracies are propagated by performing a damage assessment on two example components. The results show that, for the scenario under investigation, Arias intensity combined with the binned demand model provides the best risk estimates, if sufficient samples are available, whilst ground displacement and duration‐based IMs ranked worst, irrespective of the demand model. The findings highlight the importance and interconnectedness of the selection of the IM and the demand model when using cloud analysis and present a clear method of determining the most accurate combination for risk assessments. [ABSTRACT FROM AUTHOR]

We combine magnetic data from the first 46 data‐taking periapsides of the polar orbiting Juno spacecraft spanning dawn to dusk via midnight to investigate azimuthal fields and related currents in Jupiter's nightside magnetosphere. Data are binned by perpendicular radial distance ρ from the magnetic axis over 4–32 RJ along empirical poloidal model field lines spanning from tail to middle magnetosphere regions (ionospheric colatitudes θi ∼ 5°–17°), and by local time (LT). The data are well organized by these parameters. On southern tail field lines (∼5°<θi≤11° ${\sim 5{}^{\circ}< \theta }_{i}\le 11{}^{\circ}$) the azimuthal field is well represented as the sum of sweepback fields falling as 1/ρ that are near‐independent of θi and LT, and a near‐constant field consistent with ∼3.5 nT pointing sunward. The combination is swept back at dawn/midnight but swept forward at dusk outside ∼5 RJ. Outer magnetosphere (θi ∼ 12°–15.5°) azimuthal fields are instead swept back near‐independent of LT, near‐continuous with the tail field in the dawn sector, but with large shear at the tail interface and across outer magnetosphere field lines in the dusk‐midnight sector. The tail region 1/ρ field is associated with a nightside inward polar axial current ∼5.8 MA located within θi ∼ 5° of the magnetic axis, while the dusk‐midnight field shear measured near the ∼30 RJ study boundary provides an inward current ∼15.4 MA, related to the near‐constant field. Azimuthal fields fall to small values across middle magnetosphere field lines (θi ∼ 15.5°–17°), associated with outward currents ∼21.2 MA per hemisphere near‐independent of LT forming the nightside equatorial current sheet, balancing these inward currents. Key Points: We combine magnetic data from 46 Juno periapsides using a poloidal field model to examine Jupiter's nightside azimuthal fields to 32 RJOuter magnetosphere region fields at ∼30 RJ are swept back independent of LT associated with outward radial equatorial currents ∼13 MA rad−1Tail region fields are swept‐back at dawn/midnight but swept forward at dusk implying strong tail boundary inward currents ∼10 MA rad−1 [ABSTRACT FROM AUTHOR]

The novel object recognition (NOR) paradigm is a cognitive test that has been used with many species to detect differences in ability. Various iterations of the paradigm have been implemented, making it difficult to compare results both within and across species. Interpretations of the results are equally diverse, threatening the integrity of the paradigm. These inconsistencies have prompted a deeper dive into the variability of the resultant data. For the purposes of this meta-analysis, data originated from 12 studies involving 367 pigs that were subjected to the same NOR paradigm beginning between postnatal days 21 and 24. The main cognitive measure from the NOR paradigm is recognition index (RI), which was the focus of most of the analyses in this meta-analysis. RI was chosen as the main outcome as it determines a pig's preference for novelty, an innate behavior of cognitively intact pigs. A histogram of RI values (range 0 to 1) showed a bimodal distribution skewed to the right, suggesting that the interpretation of positive performance on the task may need to be stricter. Correlational analyses proved that the number of investigations and investigation time with both the novel and familiar objects were the strongest predictors of resultant RI values. Objective data inclusion criteria were then considered to eliminate non-compliant pigs. Results indicated that requiring at least 5 s of investigation over a minimum of 3 investigations with the novel object reduced overall variability for RI with a concomitant increase in the mean. Further analyses showed that pigs preferred to spend more time with and interact more with the novel object across the entire testing trial, especially in the first minute. Together, these findings suggest that future interpretations of NOR should consider applying stricter statistical analyses as well as additional data processing, such as binning, with emphasis on novel object and familiar object investigation. Overall, modifications to the existing iterations of the NOR paradigm are necessary to improve paradigm reliability. [ABSTRACT FROM AUTHOR]

The North Temperate Lakes Long-Term Ecological Research (NTL-LTER) program has been extensively used to improve understanding of how aquatic ecosystems respond to environmental stressors, climate fluctuations, and human activities. Here, we report on the metagenomes of samples collected between 2000 and 2019 from Lake Mendota, a freshwater eutrophic lake within the NTL-LTER site. We utilized the distributed metagenome assembler MetaHipMer to coassemble over 10 terabases (Tbp) of data from 471 individual Illumina-sequenced metagenomes. A total of 95,523,664 contigs were assembled and binned to generate 1,894 non-redundant metagenome-assembled genomes (MAGs) with ≥50% completeness and ≤10% contamination. Phylogenomic analysis revealed that the MAGs were nearly exclusively bacterial, dominated by Pseudomonadota (Proteobacteria, N = 623) and Bacteroidota (N = 321). Nine eukaryotic MAGs were identified by eukCC with six assigned to the phylum Chlorophyta. Additionally, 6,350 high-quality viral sequences were identified by geNomad with the majority classified in the phylum Uroviricota. This expansive coassembled metagenomic dataset provides an unprecedented foundation to advance understanding of microbial communities in freshwater ecosystems and explore temporal ecosystem dynamics. [ABSTRACT FROM AUTHOR]

This research proposes a new adaptive binned Kernel Density Estimation (KDE) method to fit significant wave height data in order to more accurately calculate the sea state parameter distribution tails. The brief details of the proposed new methodology are to utilize the fast Fourier transform to perform the convolution of the data with the kernel to find the kernel density estimates on the bin nodes. The Crámer-von Mises test statistic for the proposed adaptive KDE distribution is calculated to be 0.0926 based on a measured wave data set. The Crámer-von Mises test statistic value for the fitted 3-parameter Weibull distribution based on the identical measured data set is 251.2463, indicating a much poorer fit to the empirical distribution function. The advantages of using a more reliable and accurate contour line derived using the proposed new method for long term reliability analysis of wave energy converters have been finally substantiated. [ABSTRACT FROM AUTHOR]

Single-molecule and related experiments yield time series of an observable as it fluctuates due to thermal motion. In such data, it can be difficult to distinguish fluctuating signal from fluctuating noise. We present a method of separating signal from noise using nonlinear-correlation functions. The method is fully nonparametric: No a priori model for the system is required, no knowledge of whether the system is continuous or discrete is needed, the number of states is not fixed, and the system can be Markovian or not. The noise-corrected, nonlinear-correlation functions can be converted to the system's Green's function; the noise-corrected moments yield the system's equilibrium-probability distribution. As a demonstration, we analyze synthetic data from a three-state system. The correlation method is compared to another fully nonparametric approach—time binning to remove noise, and histogramming to obtain the distribution. The correlation method has substantially better resolution in time and in state space. We develop formulas for the limits on data quality needed for signal recovery from time series and test them on datasets of varying size and signal-to-noise ratio. The formulas show that the signal-to-noise ratio needs to be on the order of or greater than one-half before convergence scales at a practical rate. With experimental benchmark data, the positions and populations of the states and their exchange rates are recovered with an accuracy similar to parametric methods. The methods demonstrated here are essential components in building a complete analysis of time series using only high-order correlation functions. [ABSTRACT FROM AUTHOR]

The article describes a methodology for using large language models (LLM) to create user-friendly applications for redetermination of Medicaid eligibility and other social services in Washington, North Dakota and South Carolina. The study evaluated the accuracy of the Python code when it comes to identifying which specific Medicaid program the user is eligible for. It examined the effects of two programmer-set parameters, which are temperature and top p, on results.

Context. The existence of a plateau in the short-duration tail of the observed distribution of cosmological long-soft gamma ray bursts (LGRBs) is posited to be the first direct evidence of collapsars. A similar plateau in the short-duration tail of the observed duration distribution of short-hard gamma ray bursts (SGRBs) has been suggested as evidence of compact binary mergers. Aims. We present an equally plausible alternative interpretation for this evidence, based on a purely statistical approach. Methods. Specifically, we show that the observed plateau in the short-duration tail of the duration distribution of LGRBs can naturally occur in the statistical distributions of strictly positive physical quantities, exacerbated by the effects of mixing with the duration distribution of SGRBs, observational selection effects, and data aggregation (e.g., binning) methodologies. The observed plateau in the short-duration tail of the observed distributions of SGRBs may likewise result from a combination of sample incompleteness and inhomogeneous binning of data. We further confirm the impact of these factors on the observation of a plateau in the duration distributions of GRBs through extensive numerical Monte Carlo simulations. Results. This analysis corroborates and strengthens a purely statistical and sample-incompleteness interpretation of the observed plateau in the duration distribution of LGRBs and SGRBs, without invoking the physics of collapsars or jet-propagation through the stellar envelope. [ABSTRACT FROM AUTHOR]

The estimation of the slope (b -value) of the frequency–magnitude distribution of earthquakes is based on a formula derived by Aki decades ago, assuming a continuous exponential distribution. However, as the magnitude is usually provided with a limited resolution, its distribution is not continuous but discrete. In the literature, this problem was initially solved by an empirical correction (due to Utsu) to the minimum magnitude, and later by providing an exact formula such as that by Tinti and Mulargia, based on the geometric distribution theory. A recent paper by van der Elst showed that the b -value can be estimated also by considering the magnitude differences (which are proven to follow an exponential discrete Laplace distribution) and that in this case the estimator is more resilient to the incompleteness of the magnitude data set. In this work, we provide the complete theoretical formulation including (i) the derivation of the means and standard deviations of the discrete exponential and Laplace distributions; (ii) the estimators of the decay parameter of the discrete exponential and trimmed Laplace distributions and (iii) the corresponding formulas for the parameter b. We deduce (iv) the standard 1 σ intervals for the estimated b. Moreover, we are able (v) to quantify the error associated with the Utsu minimum-magnitude correction. Furthermore, we have discussed the formulas to produce statistically independent magnitude differences. We tested extensively the b -value estimators on simulated synthetic data sets including complete catalogues as well as catalogues affected by a strong incompleteness degree such as aftershock sequences where the incompleteness is made to vary from one event to the next. We have also analysed the real aftershock sequence of the 30/10/2016 Norcia (central Italy) to integrate the finding of the simulations. To judge the performance of the various estimators we have introduced an index p that can be seen as a non-parametric extension of the Student's t index. The main outcomes of this paper are that (1) the b -value estimators devised for continuous magnitude data are not adequate for binned magnitudes, (2) for complete data sets, estimators based on magnitudes and on magnitude differences provide substantially equivalent results, (3) for incomplete magnitude data sets, estimators based on magnitude differences provide better results and (4) for incomplete aftershock sequences there is no evidence that methods based on positive magnitude differences are superior than other methods using differences. This conclusion is further confirmed by our analysis of the above-mentioned Norcia seismic sequence. This last finding contrasts with the van der Elst's claim that the so called |${{b}_ + }$| method is the most adequate to treat real aftershock sequences. [ABSTRACT FROM AUTHOR]

Differential privacy, a cornerstone of privacy-preserving techniques, plays an indispensable role in ensuring the secure handling and sharing of sensitive data analysis across domains such as in census, healthcare, and social networks. Histograms, serving as a visually compelling tool for presenting analytical outcomes, are widely employed in these sectors. Currently, numerous algorithms for publishing histograms under differential privacy have been developed, striving to balance privacy protection with the provision of useful data. Nonetheless, the pivotal challenge concerning the effective enhancement of precision for small bins (those intervals that are narrowly defined or contain a relatively small number of data points) within histograms has yet to receive adequate attention and in-depth investigation from experts. In standard DP histogram publishing, adding noise without regard for bin size can result in small data bins being disproportionately influenced by noise, potentially severely impairing the overall accuracy of the histogram. In response to this challenge, this paper introduces the SReB_GCA sanitization algorithm designed to enhance the accuracy of small bins in DP histograms. The SReB_GCA approach involves sorting the bins from smallest to largest and applying a greedy grouping strategy, with a predefined lower bound on the mean relative error required for a bin to be included in a group. Our theoretical analysis reveals that sorting bins in ascending order prior to grouping effectively prioritizes the accuracy of smaller bins. SReB_GCA ensures strict ϵ -DP compliance and strikes a careful balance between reconstruction error and noise error, thereby not only initially improving the accuracy of small bins but also approximately optimizing the mean relative error of the entire histogram. To validate the efficiency of our proposed SReB_GCA method, we conducted extensive experiments using four diverse datasets, including two real-life datasets and two synthetic ones. The experimental results, quantified by the Kullback–Leibler Divergence (KLD), show that the SReB_GCA algorithm achieves substantial performance enhancement compared to the baseline method (DP_BASE) and several other established approaches for differential privacy histogram publication. [ABSTRACT FROM AUTHOR]

Ozone trends in the upper troposphere and lower stratosphere (UTLS) remain highly uncertain because of sharp spatial gradients and high variability caused by competing transport, chemical, and mixing processes near the upper-tropospheric jets and extratropical tropopause as well as inhomogeneous spatially and temporally limited observations of the region. Subtropical jets and the tropopause act as transport barriers, delineating boundaries between atmospheric regimes controlled by different processes; they can thus be used to separate data taken in those different regimes for numerous purposes, including trend assessment. As part of the Observed Composition Trends And Variability in the UTLS (OCTAV-UTLS) Stratosphere-troposphere Processes And their Role in Climate (SPARC) activity, we assess the effectiveness of several coordinate systems in segregating air into different atmospheric regimes. To achieve this, a comprehensive dynamical dataset is used to reference every measurement from various observing systems to the locations of jets and tropopauses in different coordinates (e.g., altitude, pressure, potential temperature, latitude, and equivalent latitude). We assess which coordinate combinations are most useful for dividing the measurements into bins such that the data in each bin are affected by the same processes, thus minimizing the variability induced when combining measurements from different dynamical regimes, each characterized by different physical processes. Such bins will be particularly suitable for combining measurements with different sampling characteristics and for assessing trends and attributing them to changing atmospheric dynamics. Overall, the use of equivalent latitude and potential temperature leads to the most substantial reduction in binned variability across the UTLS. This coordinate pairing uses potential vorticity (PV) on isentropic surfaces, thus aligning with the adiabatic transport of tracers. [ABSTRACT FROM AUTHOR]

Purpose: To develop an iterative concomitant field and motion corrected (iCoMoCo) reconstruction for isotropic high‐resolution UTE pulmonary imaging at 0.55 T. Methods: A free‐breathing golden‐angle stack‐of‐spirals UTE sequence was used to acquire data for 8 min with prototype and commercial 0.55 T MRI scanners. The data was binned into 12 respiratory phases based on superior–inferior navigator readouts. The previously published iterative motion corrected (iMoCo) reconstruction was extended to include concomitant field correction directly in the cost function. The reconstruction was implemented within the Gadgetron framework for inline reconstruction. Data were retrospectively reconstructed to simulate scan times of 2, 4, 6, and 8 min. Image quality was assessed using apparent SNR and image sharpness. The technique was evaluated in healthy volunteers and patients with known lung pathology including coronavirus disease 2019 infection, chronic granulomatous disease, lymphangioleiomyomatosis, and lung nodules. Results: The technique provided diagnostic‐quality images, and image quality was maintained with a slight loss in SNR for simulated scan times down to 4 min. Parenchymal apparent SNR was 4.33 ± 0.57, 5.96 ± 0.65, 7.36 ± 0.64, and 7.87 ± 0.65 using iCoMoCo with scan times of 2, 4, 6, and 8 min, respectively. Image sharpness at the diaphragm was comparable between iCoMoCo and reference images. Concomitant field corrections visibly improved the sharpness of anatomical structures away from the isocenter. Inline image reconstruction and artifact correction were achieved in <5 min. Conclusion: The proposed iCoMoCo pulmonary imaging technique can generate diagnostic quality images with 1.75 mm isotropic resolution in less than 5 min using a 6‐min acquisition, on a 0.55 T scanner. [ABSTRACT FROM AUTHOR]

Background: The literature for assessing online and offline shopping behaviours that are linked to intelligent robotic goods and services is inadequate. In this study, we applied the Theory of Planned Behaviour model for guidance regarding how consumer behaviour affects their purchase intentions for intelligent robotic goods and services. Methods: Data from 408 respondents were gathered through an online questionnaire binned into Online and Overall Shoppers, and analysed using SPSS, AMOS, and Covariance-Based Structural Equation Modelling software to evaluate the appropriateness of the measurements and to confirm data reliability, convergence, divergence, and validity. These tools were also used to track and test hypothesized relationships between the variables and model constructs used in this study. Results and conclusions: The overarching outcomes from the data analyses indicated the Ease of Usage, Brand Perception, and Product Pricing variables causally impacted the TPB model constructs, namely Attitude, Subjective Norms, and Perceived Behaviour Control for the two populations tested with respect to their intention to purchase intelligent robotic goods and services. The reliability measurements for Ease of Usage, Brand Perception, and Product Pricing are discussed. The results are important for companies and future investors because opportunities to study the complex relationships that ultimately drive consumer behaviour and their intention to purchase intelligent robotic goods and services are provided. [ABSTRACT FROM AUTHOR]

Covariance function estimation is a fundamental task in multivariate functional data analysis and arises in many applications. In this article, we consider estimating sparse covariance functions for high-dimensional functional data, where the number of random functions p is comparable to, or even larger than the sample size n. Aided by the Hilbert–Schmidt norm of functions, we introduce a new class of functional thresholding operators that combine functional versions of thresholding and shrinkage, and propose the adaptive functional thresholding estimator by incorporating the variance effects of individual entries of the sample covariance function into functional thresholding. To handle the practical scenario where curves are partially observed with errors, we also develop a nonparametric smoothing approach to obtain the smoothed adaptive functional thresholding estimator and its binned implementation to accelerate the computation. We investigate the theoretical properties of our proposals when p grows exponentially with n under both fully and partially observed functional scenarios. Finally, we demonstrate that the proposed adaptive functional thresholding estimators significantly outperform the competitors through extensive simulations and the functional connectivity analysis of two neuroimaging datasets. for this article are available online. [ABSTRACT FROM AUTHOR]

Presently, it is unclear whether the Eddington ratio (λ) and radiative efficiency (ε) depend on a supermassive black hole's (SMBH's) redshift z and mass M BH. We attempt to resolve this issue using published data for 132   000 SMBHs with M BH ≥ 107 Msun (solar masses) at ∼0.1 <  z  < 2.4 covering ∼10 billion years of cosmic time, with M BH determined using Mg  ii lines and bolometric luminosities L bol based on a weighted mean of L bol from two or more monochromatic luminosities and a single uniformly applied correction factor. The SMBHs are sorted into seven M BH bins separated from each other by half an order of magnitude. The λ and z data in each bin are subjected to spline regression analysis. The results unambiguously show that for similar-size SMBHs, λ decreases as z decreases, and that for a given redshift, larger SMBHs have a lower λ. These findings require that either an SMBH's accretion rate and/or its radiative efficiency be a function of z and M BH and, in the context of the Bondi accretion model, imply that radiative efficiency is an inverse function of z and M BH. These findings suggest that SMBHs become less efficient (higher ε) in accreting gases as the ambient gas density decreases with z and that larger SMBHs are more efficient (lower ε) than smaller ones. The results leave little doubt that the current widespread practice of assigning ε a standard value is untenable and gives erroneous estimates of accretion rates and growth times of SMBHs. [ABSTRACT FROM AUTHOR]

This work analyzes the present-day mass function (PDMF) of 93 star clusters utilizing Gaia Data Release 3 data, with membership determined by the StarGo machine-learning algorithm. The impact of unresolved binary systems on mass estimation is rigorously assessed, adopting three mass ratio profiles for correction. The PDMF is characterized by the power-law index, α, derived through a robust maximum likelihood method that avoids biases associated with data binning. The value of α for stars between the completeness limited mass of Gaia (with a mean 0.3 M ⊙ for our cluster samples) and 2 M ⊙ exhibits stability for clusters younger than 200 Myr, decreasing for older clusters, particularly when considering stars within the half-mass radius. The PDMF of these star clusters is consistent with a dynamically evolved Kroupa initial mass function via the loss of low-mass stars. Cluster morphology shows a correlation with α, as α values exhibit a decreasing trend from filamentary to tidal-tail clusters, mirroring the sequence of increasing cluster age. The dependence of α on the total cluster mass is weak, with a subtle increase for higher-mass clusters, especially outside the half-mass radius. We do not observe a correlation between α and the mean metallicity of the clusters. Younger clusters have lower metallicity compared to their older counterparts, which indicates that the older clusters might have migrated to the solar neighborhood from the inner disk. A comparison with numerical models incorporating a black hole population suggests the need for observations of distant, older, massive open clusters to determine whether or not they contain black holes. [ABSTRACT FROM AUTHOR]

Using information-theoretic quantities in practical applications with continuous data is often hindered by the fact that probability density functions need to be estimated in higher dimensions, which can become unreliable or even computationally unfeasible. To make these useful quantities more accessible, alternative approaches such as binned frequencies using histograms and k-nearest neighbors (k-NN) have been proposed. However, a systematic comparison of the applicability of these methods has been lacking. We wish to fill this gap by comparing kernel-density-based estimation (KDE) with these two alternatives in carefully designed synthetic test cases. Specifically, we wish to estimate the information-theoretic quantities: entropy, Kullback–Leibler divergence, and mutual information, from sample data. As a reference, the results are compared to closed-form solutions or numerical integrals. We generate samples from distributions of various shapes in dimensions ranging from one to ten. We evaluate the estimators' performance as a function of sample size, distribution characteristics, and chosen hyperparameters. We further compare the required computation time and specific implementation challenges. Notably, k-NN estimation tends to outperform other methods, considering algorithmic implementation, computational efficiency, and estimation accuracy, especially with sufficient data. This study provides valuable insights into the strengths and limitations of the different estimation methods for information-theoretic quantities. It also highlights the significance of considering the characteristics of the data, as well as the targeted information-theoretic quantity when selecting an appropriate estimation technique. These findings will assist scientists and practitioners in choosing the most suitable method, considering their specific application and available data. We have collected the compared estimation methods in a ready-to-use open-source Python 3 toolbox and, thereby, hope to promote the use of information-theoretic quantities by researchers and practitioners to evaluate the information in data and models in various disciplines. [ABSTRACT FROM AUTHOR]

Metagenomic analysis typically includes read-based taxonomic profiling, assembly, and binning of metagenome-assembled genomes (MAGs). Here we integrate these steps in Read Annotation Tool (RAT), which uses robust taxonomic signals from MAGs and contigs to enhance read annotation. RAT reconstructs taxonomic profiles with high precision and sensitivity, outperforming other state-of-the-art tools. In high-diversity groundwater samples, RAT annotates a large fraction of the metagenomic reads, calling novel taxa at the appropriate, sometimes high taxonomic ranks. Thus, RAT integrative profiling provides an accurate and comprehensive view of the microbiome from shotgun metagenomics data. The package of Contig Annotation Tool (CAT), Bin Annotation Tool (BAT), and RAT is available at https://github.com/MGXlab/CAT%5fpack (from CAT pack v6.0). The CAT pack now also supports Genome Taxonomy Database (GTDB) annotations. Metagenomic taxonomic profiling usually relies either on reads or assembled contigs/MAGs. Here, authors present RAT, a tool that integrates taxonomic signals from reads, contigs, and MAGs into one profile with high precision and sensitivity. RAT provides a comprehensive view of the microbiome. [ABSTRACT FROM AUTHOR]

This study aims to examine the individual and combined effects of soil moisture (SM) and vapor pressure deficit (VPD) on ecosystem productivity in Southwest China. Utilizing the community land model (CLM) to simulate the regional soil moisture and vapor pressure deficit, we analyzed their impacts on ecosystem productivity through a data binning approach and employed sun-induced chlorophyll fluorescence yield (SIFyield) as a productivity indicator. Our findings highlight a significant coupling effect between SM and VPD, which diminishes with finer temporal data resolution. The data binning analysis indicates that VPD has a predominant influence on SIFyield across 70% of the study area, whereas SM is more influential in the remaining 30%. Notably, the correlation between SIFyield and SM, modulated by VPD, is stronger in forest and shrubland ecosystems, whereas in grasslands, the influence pattern is reversed, with VPD having a more significant impact. The study concludes that in Southwest China, ecosystem productivity is more significantly affected by VPD than by SM. [ABSTRACT FROM AUTHOR]

In this paper, we study an isotropic flat FLRW-model in scale-covariant theory of gravity f γ δ (ϕ) 1 which is explained in terms of ordinary and covariant differentiation of scalar field ϕ. As we know, the deceleration parameter (DP) is time-dependent, so we consider the DP q as the function of t. Using this methodology, we find all the important cosmological factors in terms of a hyperbolic function of the cosmic time t. In turn, we create the model having the behavior of the late-time universe, which is ever accelerated expanding and faces a Big Freeze at the end. The model shows the quintessence dark energy model from early to late times. We compute the constrained values of Hubble parameter H 0 = 7 0. 9 7 9 − 0. 0 0 4 3 + 0. 0 2 1 and the model parameter n = 1. 2 4 0 7 9 − 0. 0 0 0 7 9 + 0. 0 0 0 1 5 using joint analysis of the OHD data of 77-points and Pantheon bin data. The model exhibits point-type singularity as it begins with a point of zero volume, infinite energy density and temperature. Furthermore, we obtain the present value of DP (q 0) ≈ − 0. 5 4. Also, we examine the ultimate behavior of our model by the proper analysis of energy conditions (ECs), cosmographical parameters and Statefinder diagnostic. Finally, the proposed model behaves like a quintessence dark energy model. [ABSTRACT FROM AUTHOR]

We use the SAMI (Sydney-AAO Multi-object Integral field spectrograph) galaxy survey to study the the kinematic morphology–density relation: the observation that the fraction of slow rotator galaxies increases towards dense environments. We build a logistic regression model to quantitatively study the dependence of kinematic morphology (whether a galaxy is a fast or slow rotator) on a wide range of parameters, without resorting to binning the data. Our model uses a combination of stellar mass, star formation rate (SFR), r -band half-light radius, and a binary variable based on whether the galaxy's observed ellipticity (ϵ) is less than 0.4. We show that, at fixed mass, size, SFR, and ϵ, a galaxy's local environmental surface density (log10(Σ5/Mpc−2)) gives no further information about whether a galaxy is a slow rotator, that is, the observed kinematic–morphology density relation can be entirely explained by the well-known correlations between environment and other quantities. We show how our model can be applied to different galaxy surveys to predict the fraction of slow rotators which would be observed and discuss its implications for the formation pathways of slow rotators. [ABSTRACT FROM AUTHOR]

Beneficial bacteria remain largely unexplored. Lacking systematic methods, understanding probiotic community traits becomes challenging, leading to various conclusions about their probiotic effects among different publications. We developed language model–based metaProbiotics to rapidly detect probiotic bins from metagenomes, demonstrating superior performance in simulated benchmark datasets. Testing on gut metagenomes from probiotic-treated individuals, it revealed the probioticity of intervention strains–derived bins and other probiotic-associated bins beyond the training data, such as a plasmid-like bin. Analyses of these bins revealed various probiotic mechanisms and bai operon as probiotic Ruminococcaceae 's potential marker. In different health–disease cohorts, these bins were more common in healthy individuals, signifying their probiotic role, but relevant health predictions based on the abundance profiles of these bins faced cross-disease challenges. To better understand the heterogeneous nature of probiotics, we used metaProbiotics to construct a comprehensive probiotic genome set from global gut metagenomic data. Module analysis of this set shows that diseased individuals often lack certain probiotic gene modules, with significant variation of the missing modules across different diseases. Additionally, different gene modules on the same probiotic have heterogeneous effects on various diseases. We thus believe that gene function integrity of the probiotic community is more crucial in maintaining gut homeostasis than merely increasing specific gene abundance, and adding probiotics indiscriminately might not boost health. We expect that the innovative language model–based metaProbiotics tool will promote novel probiotic discovery using large-scale metagenomic data and facilitate systematic research on bacterial probiotic effects. The metaProbiotics program can be freely downloaded at https://github.com/zhenchengfang/metaProbiotics. [ABSTRACT FROM AUTHOR]

Purpose: Pathogen transmission during cardio-pulmonary exercise testing (CPET) is caused by carrier aerosols generated during respiration. Methods: Ten healthy volunteers (age range: 34 ± 15; 4 females) were recruited to see if the physiological reactions to ramp-incremental CPET on a cycle ergometer were affected using an in-line filter placed between the mouthpiece and the flow sensor. The tests were in random order with or without an in-line bacterial/viral spirometer filter. The work rate aligned, time interpolated 10 s bin data were compared throughout the exercise period. Results: From rest to peak exercise, filter use increased only minute ventilation ( V ˙ E) (Δ V ˙ E = 1.56 ± 0.70 L/min, P < 0.001) and tidal volume (VT) (ΔVT = 0.10 ± 0.11 L, P = 0.014). Over the entire test, the slope of the residuals for V ˙ CO2 was positive (0.035 ± 0.041 (ΔL/L), P = 0.027). During a ramp-incremental CPET in healthy subjects, an in-line filter increased V ˙ E and VT but not metabolic rate. Conclusion: In conclusion, using an in-line filter is feasible, does not affect appreciably the physiological variables, and may mitigate risk of aerosol dispersion during CPET. [ABSTRACT FROM AUTHOR]

Individual body size distributions (ISD) within communities are remarkably consistent across habitats and spatiotemporal scales and can be represented by size spectra, which are described by a power law. The focus of size spectra analysis is to estimate the exponent (λ) of the power law. A common application of size spectra studies is to detect anthropogenic pressures.Many methods have been proposed for estimating λ most of which involve binning the data, counting the abundance within bins, and then fitting an ordinary least squares regression in log–log space. However, recent work has shown that binning procedures return biased estimates of λ compared to procedures that directly estimate λ using maximum likelihood estimation (MLE). While it is clear that MLE produces less biased estimates of site‐specific λ's, it is less clear how this bias affects the ability to test for changes in λ across space and time, a common question in the ecological literature.Here, we used simulation to compare the ability of two normalised binning methods (equal logarithmic and log2 bins) and MLE to (1) recapture known values of λ, and (2) recapture parameters in a linear regression measuring the change in λ across a hypothetical environmental gradient. We also compared the methods using two previously published body size datasets across a natural temperature gradient and an anthropogenic pollution gradient.Maximum likelihood methods always performed better than common binning methods, which demonstrated consistent bias depending on the simulated values of λ. This bias carried over to the regressions, which were more accurate when λ was estimated using MLE compared to the binning procedures. Additionally, the variance in estimates using MLE methods is markedly reduced when compared to binning methods.The error induced by binning methods can be of similar magnitudes as the variation previously published in experimental and observational studies, bringing into question the effect sizes of previously published results. However, while the methods produced different regression slope estimates, they were in qualitative agreement on the sign of those slopes (i.e. all negative or all positive). Our results provide further support for the direct estimation of λ and its relative variation across environmental gradients using MLE over the more common methods of binning. [ABSTRACT FROM AUTHOR]

The objectives of this study were (i) to compare the accuracy and bias of estimates of breeding values for body weight (BW) at 2–7 weeks of age using pedigree-based best linear unbiased prediction (BLUP) and single-step genomic BLUP (ssGBLUP) methods, and (ii) to determine the best level of minor allele frequencies (MAFs) for pre-selection of SNPs for genomic prediction (GP). Records of 488 F2 broiler chickens obtained from crossbreeding of fast-growing Arian chickens and slow-growing Iranian native chickens at 2–7 weeks of age were used. Samples were genotyped using Illumina Chicken 60K BeadChip. To investigate the effect of MAFs on the accuracy of prediction, 48 379 quality-controlled SNPs were grouped into five subgroups with MAF bins 0.05–0.1, 0.1–0.2, 0.2–0.3, 0.3–0.4, and 0.4–0.5. Our results confirmed the superiority of ssGBLUP compared to traditional BLUP methodology. The average accuracy of GP improved by 59.03%, 220.34%, 0.46%, 5.61%, 0.45%, and 2.73% using ssGBLUP compared to BLUP for BW at 2–7 weeks of age, respectively. Depending on the age group, using a subset of SNPs with a specific MAF bin compared to all SNPs resulted in a remarkable improvement of GP accuracy for the observed traits. [ABSTRACT FROM AUTHOR]

The structural integrity of safety-critical infrastructures diminishes over time, necessitating periodic inspections. The gathering of low-noise, precise nondestructive evaluation (NDE) readings for defect detection can be both time-consuming and costly. In practical applications, NDE data can be highly noisy due to factors like liftoff/stand-off distances, probe drift, scanning speed, and variations in data acquisition rates. Given the broad spectrum of potential uncertainties that can contribute to noise accumulation, characterizing the noise in such NDE data presents significant difficulties. As a result, the extraction of defect signals through deconvolution becomes challenging. This article introduces an NDE-based dynamic defect tracking framework designed for robust conditions. It incorporates regular periodic monitoring of the material under test (MUT) using a cost-effective, easy-to-implement magnetic flux leakage (MFL) probe. The framework proposes a dynamic setup that includes occasional, highly accurate, expensive MFL scans among regular, low-cost, noisy MFL scans. The objective is to detect and precisely track defect formation in metallic pipes over time, thereby enabling the efficient alarming and localization of defects before they reach detrimental sizes. A key characteristic of the proposed dynamic monitoring method is its noise type agnosticism in the low-cost MFL scans. It remains effective even when the underlying noise-producing uncertainties fluctuate significantly over time, a feature achieved through the use of transfer learning. The method transfers accurate information regarding the locations and sizes of existing corrosions from the detailed MFL scan to the subsequent noisy MFL inspections. With this supportive information from defective scan-points, it estimates the degradation of the signal-to-noise ratio (SNR) in the noisy MFL scans. The framework suggests binned hypothesis tests in noisy MFL scans, setting the level of aggregation based on the SNR estimate in the scan data. This proposed binned hypothesis test optimizes defect coverage and maintains control over the false discovery rate (FDR). Experimental MFL data, gathered under a wide range of uncertainties and defect types, illustrates the highly heterogeneous nature of noise distributions in low-cost MFL scans and the significant variance in SNR deteriorations. The necessity of using binned hypothesis tests in these low-cost MFL data is highlighted by the poor performance of pointwise multiple hypothesis tests. Finally, the high effectiveness of the proposed transfer learning-based binned hypothesis testing method is demonstrated. Although the method was applied to MFL data in this instance, it is also suitable for other NDE applications. The applicability of the algorithms has also been confirmed through validation on experimentally generated Eddy Current (EC) data. [ABSTRACT FROM AUTHOR]

Far-infrared observations from the Herschel Space Observatory are used to estimate the infrared (IR) properties of ultraviolet-selected galaxies. We stack the PACS (100, 160  |$\mu$| m) and SPIRE (250, 350, and 500  |$\mu$| m) maps of the Chandra deep field south (CDFS) on a source list of galaxies selected in the rest-frame ultraviolet (UV) in a redshift range of 0.6–1.2. This source list is created using observations from the XMM–OM telescope survey in the CDFS using the UVW1 (2910 Å) filter. The stacked data are binned according to the UV luminosity function of these sources, and the average photometry of the UV-selected galaxies is estimated. By fitting modified black bodies and IR model templates to the stacked photometry, average dust temperatures and total IR luminosity are determined. The luminosity-weighted average temperatures are consistent with a weak trend of increasing temperature with redshift found by previous studies. Infrared excess, unobscured, and obscured star formation rate (SFR) values are obtained from the UV and IR luminosities. We see a trend in which dust attenuation increases as UV luminosity decreases. It remains constant as a function of IR luminosities at fixed redshift across the luminosity range of our sources. In comparison to local luminous infrared galaxies with similar SFRs, the higher redshift star-forming galaxies in the sample show a lesser degree of dust attenuation. Finally, the inferred dust attenuation is used to correct the unobscured SFR density in the redshift range 0.6–1.2. The dust-corrected SFR density is consistent with measurements from IR-selected samples at similar redshifts. [ABSTRACT FROM AUTHOR]

Ozone trends in the upper troposphere/lower stratosphere (UTLS) remain highly uncertain because of sharp spatial gradients and large variability caused by competing transport, chemical, and mixing processes near the upper tropospheric jets and extra-tropical tropopause, as well as inhomogeneous spatially and temporally limited observations of the region. Subtropical jets and the tropopause act as transport barriers, delineating boundaries between atmospheric regimes controlled by different processes; they can thus be used to separate data taken in those different regimes for numerous purposes, including trend assessment. As part of the Observed Composition Trends And Variability in the UTLS (OCTAV-UTLS) Stratosphere-troposphere Processes And their Role in Climate (SPARC) activity, we assess the effectiveness of several coordinate systems in segregating air into different atmospheric regimes. To achieve this, a comprehensive dynamical dataset is used to reference every measurement from various observing systems to the locations of jets and tropopauses in different coordinates (e.g., altitude, pressure, potential temperature, latitude, and equivalent latitude). We assess which coordinate combinations are most useful for dividing the measurements into bins such that the data in each bin is affected by the same processes, thus minimizing the variability induced when combining measurements from different dynamical regimes, each characterized by different physical processes. Such bins will be particularly suitable for combining measurements with different sampling characteristics and for assessing trends and attributing them to changing atmospheric dynamics. [ABSTRACT FROM AUTHOR]

Background: Photon counting detectors (PCDs) for x‐ray computed tomography (CT) face spectral distortion from pulse pileup and charge sharing. The photon counting scheme used by many PCDs is threshold–subtract (TS) with pulse height analysis (PHA), where each counter counts up‐crossing events when pulses exceed an energy threshold. PCD data are not Poisson‐distributed due to charge sharing and pulse pileup, but the counting statistics have never been studied yet. Purpose: The objectives of this study were (1) to propose a modified photon counting scheme, direct energy binning (DB), that is expected to be robust against pulse pileup; (2) to assess the performance of DB compared to TS; and (3) to evaluate its counting statistics. Methods: With DB scheme, counter k starts a timer upon an up‐crossing event of energy threshold k, and adds a count only if the next higher energy threshold (k+1) was not crossed within a short time window (hence, the pulse peak belongs to the energy bin k). We used Monte Carlo (MC) simulation and assessed count‐rate curves and count‐rate‐dependent spectral imaging task performance for conventional CT imaging as well as water thickness estimation, water–bone material decomposition, and K‐edge imaging with tungsten as the K‐edge material. We also assessed count‐rate‐dependent measurement statistics such as expectation, variance, and covariance of total counts as well as energy bin outputs. The agreement with counting statistics models was also evaluated. Results: The DB scheme improved the count‐rate curve, that is, mean measured counts as a function of input count‐rate, and peaked with 59% higher count‐rate capability than the TS scheme (3.5 × 108 counts per second (cps)/mm2 versus 2.3 × 108 cps/mm2). The Cramér–Rao lower bounds (CRLB) of the variance of basis line integrals estimation for DB was better than those for TS by 2% for the conventional CT imaging, 30% for water–bone material decomposition, and 32% for K‐edge imaging at 1000 mA (at 7.3 × 107 cps/sub‐pixel after charge sharing). When count‐rates were lower, PCD data statistics were dominated by charge sharing: the variance of total counts and lower energy bins was larger than the mean counts; the covariance of bin data was positive and non‐zero. When count‐rates were higher, PCD data statistics were dominated by pulse pileup: the variance of data was lower than the mean; the covariance of bin data was negative. The transition between the two regimes occurred smoothly, and pulse pileup dominated the statistics ≥400 mA (when the count‐rate after charge sharing was 2.9 × 107 cps/sub‐pixel and the probability of count‐loss for DB was 37%). Both DB and TS had good agreement with Yu–Fessler's models of total counts; however, DB had a better agreement with Wang's variance and covariance models for energy bin data than TS did. Conclusions: The proposed DB scheme had several advantages over TS. At low to moderate flux, DB could improve the resilience of PCDs to pulse pileup. Counting statistics deviated from the Poisson distribution due to charge sharing for lower count‐rate conditions and pulse pileup for higher count‐rate conditions. [ABSTRACT FROM AUTHOR]

In this paper we present Spatial-KWD, a free open-source tool for efficient computation of the Kantorovich-Wasserstein Distance (KWD), also known as Earth Mover Distance, between pairs of binned spatial distributions (histograms) of a non-negative variable. KWD can be used in spatial statistics as a measure of (dis)similarity between spatial distributions of physical or social quantities. KWD represents the minimum total cost of moving the "mass" from one distribution to the other when the "cost" of moving a unit of mass is proportional to the euclidean distance between the source and destination bins. As such, KWD captures the degree of "horizontal displacement" between the two input distributions. Despite its mathematical properties and intuitive physical interpretation, KWD has found little application in spatial statistics until now, mainly due to the high computational complexity of previous implementations that did not allow its application to large problem instances of practical interest. Building upon recent advances in Optimal Transport theory, the Spatial-KWD library allows to compute KWD values for very large instances with hundreds of thousands or even millions of bins. Furthermore, the tool offers a rich set of options and features to enable the flexible use of KWD in diverse practical applications. [ABSTRACT FROM AUTHOR]

Purpose: To propose a quantitative 3D double‐echo steady‐state (DESS) sequence that offers rapid and repeatable T2 mapping of the human brain using different encoding schemes that account for respiratory B0 variation. Methods: A retrospective self‐gating module was firstly implemented into the standard DESS sequence in order to suppress the respiratory artifact via data binning. A compressed‐sensing trajectory (CS‐DESS) was then optimized to accelerate the acquisition. Finally, a spiral Cartesian encoding (SPICCS‐DESS) was incorporated to further disrupt the coherent respiratory artifact. These different versions were compared to a standard DESS sequence (fully DESS) by assessing the T2 distribution and repeatability in different brain regions of eight volunteers at 3 T. Results: The respiratory artifact correction was determined to be optimal when the data was binned into seven respiratory phases. Compared to the fully DESS, T2 distribution was improved for the CS‐DESS and SPICCS‐DESS with interquartile ranges reduced significantly by a factor ranging from 2 to 12 in the caudate, putamen, and thalamus regions. In the gray and white matter areas, average absolute test–retest T2 differences across all volunteers were respectively 3.5 ± 2% and 3.1 ± 2.1% for the SPICCS‐DESS, 4.6 ± 4.6% and 4.9 ± 5.1% for the CS‐DESS, and 15% ± 13% and 7.3 ± 5.6% for the fully DESS. The SPICCS‐DESS sequence's acquisition time could be reduced by half (<4 min) while maintaining its efficient T2 mapping. Conclusion: The respiratory‐resolved SPICCS‐DESS sequence offers rapid, robust, and repeatable 3D T2 mapping of the human brain, which can be especially effective for longitudinal monitoring of cerebral pathologies. [ABSTRACT FROM AUTHOR]

Accurate prediction of part obsolescence is critical to maintaining system health, especially for the long-lived systems typical in aerospace and naval domains. While there are methods that predict an expected date of obsolescence, a numerical likelihood of obsolescence can be useful. This work describes a Weibull-based conditional probability method for the prediction of part-level obsolescence risk. Several considerations inherent to the problem environment and using a probabilistic method to estimate risk are discussed and addressed, including accounting for changing product life, using dynamic binning and Weibull regression; sample bias, through data cleaning; and small datasets with potentially highly censored data, using a modified synthetic minority oversampling technique (SMOTE) that can sample both the minority and majority classes. Development of an approximate measure of uncertainty of obsolescence is also presented. Use of the method is demonstrated with a multiplexer dataset and shows the feasibility of the approach. [ABSTRACT FROM AUTHOR]

The objective was to assess the effect of peri-implantitis surgery on the peri-implant microbiome with a follow-up of one year. A total of 25 peri-implantitis patients in whom non-surgical treatment has failed to solve peri-implantitis underwent resective surgical treatment. Their peri-implant pockets were sampled prior to surgical treatment (T0) and one year post treatment (T12). The natural dentition was sampled to analyse similarities and differences with the peri-implantitis samples. Treatment success was recorded. The change in microbial relative abundance levels was evaluated. The microbiota was analysed by sequencing the amplified V3-V4 region of the 16S rRNA genes. Sequence data were binned to amplicon sequence variants that were assigned to bacterial genera. Group differences were analysed using principal coordinate analysis, Wilcoxon signed rank tests, and t-tests. Beta diversity analyses reported a significant separation between peri-implantitis and natural dentition samples on T0 and T12, along with significant separations between successfully and non-successfully treated patients. Eubacterium was significantly lower on T12 compared to T0 for the peri-implantitis samples. Treponema and Eubacterium abundance levels were significantly lower in patients with treatment success on T0 and T12 versus no treatment success. Therefore, lower baseline levels of Treponema and Eubacterium seem to be associated with treatment success of peri-implantitis surgery. This study might aid clinicians in determining which peri-implantitis cases might be suitable for treatment and give a prognosis with regard to treatment success. [ABSTRACT FROM AUTHOR]

Photoemission electron microscopy (PEEM) is a powerful technique for surface characterization that provides detailed information on the chemical and structural properties of materials at the nanoscale. In this study, the potential is explored using a machine learning algorithm called self‐organizing map with a relational perspective map (SOM‐RPM) for visualizing and analyzing complex PEEM‐generated datasets. The application of SOM‐RPM is demonstrated using synchrotron‐based X‐ray magnetic circular dichroism (XMCD)‐PEEM data acquired from a pyrrhotite sample. Traditional visualization approaches for XMCD‐PEEM data may not fully capture the complexity of the sample, especially in the case of heterogeneous materials. By applying SOM‐RPM to the XMCD‐PEEM data, a colored topographic map is created that represents the spectral similarities and dissimilarities among the pixels. This approach allows for a more intuitive and easily interpretable representation of the data without the need of data binning or spectral smoothing. The results of the SOM‐RPM analysis are compared to the conventional visualization approach, highlighting the advantages of SOM‐RPM in revealing features that are not readily observable in the conventional method. This study suggests that the SOM‐RPM approach can be used complimentarily for other PEEM‐based measurements, such as core level and valence band X‐ray photoelectron spectroscopy. [ABSTRACT FROM AUTHOR]

We model the stellar abundances and ages of two disrupted dwarf galaxies in the Milky Way stellar halo: Gaia -Sausage Enceladus (GSE) and Wukong/LMS-1. Using a statistically robust likelihood function, we fit one-zone models of galactic chemical evolution with exponential infall histories to both systems, deriving e-folding time-scales of τin = 1.01 ± 0.13 Gyr for GSE and |$\tau _\text{in} = 3.08^{+3.19}_{-1.16}$| Gyr for Wukong/LMS-1. GSE formed stars for |$\tau _\text{tot} = 5.40^{+0.32}_{-0.31}$| Gyr, sustaining star formation for ∼1.5–2 Gyr after its first infall into the Milky Way ∼10 Gyr ago. Our fit suggests that star formation lasted for |$\tau _\text{tot} = 3.36^{+0.55}_{-0.47}$| Gyr in Wukong/LMS-1, though our sample does not contain any age measurements. The differences in evolutionary parameters between the two are qualitatively consistent with trends with stellar mass M ⋆ predicted by simulations and semi-analytic models of galaxy formation. Our inferred values of the outflow mass-loading factor reasonably match |$\eta \propto M_\star ^{-1/3}$| as predicted by galactic wind models. Our fitting method is based only on Poisson sampling from an evolutionary track and requires no binning of the data. We demonstrate its accuracy by testing against mock data, showing that it accurately recovers the input model across a broad range of sample sizes (20 ≤ N ≤ 2000) and measurement uncertainties (0.01 ≤ σ[α/Fe], σ[Fe/H] ≤ 0.5; |$0.02 \le \sigma _{\log _{10}(\text{age})} \le 1$|⁠). Due to the generic nature of our derivation, this likelihood function should be applicable to one-zone models of any parametrization and easily extensible to other astrophysical models which predict tracks in some observed space. [ABSTRACT FROM AUTHOR]

"Shaped by the State: Toward a New Political History of the Twentieth Century" is a book that explores the relationship between the state and society in shaping political and social change in the United States. The authors argue that traditional political history has focused too narrowly on electoral politics and policy-making, and they advocate for a more inclusive approach that considers the role of social movements, grassroots activism, and cultural shifts. The book challenges conventional narratives of political history and offers a more nuanced understanding of the complex dynamics between the state and society. [Extracted from the article]

We study the |$M_{\rm H\, {\small {I}}}-M_{\star }$| relation over the last billion years using the MIGHTEE-H  i sample. We first model the upper envelope of the |$M_{\rm H\, {\small {I}}}-M_{\star }$| relation with a Bayesian technique applied to a total number of 249 H  i -selected galaxies, without binning the data sets, while taking account of the intrinsic scatter. We fit the envelope with both linear and non-linear models, and find that the non-linear model is preferred over the linear one with a measured transition stellar mass of log10(M ⋆/M⊙) = 9.15 ± 0.87, beyond which the slope flattens. This finding supports the view that the lack of H  i gas is ultimately responsible for the decreasing star formation rate observed in the massive main-sequence galaxies. For spirals alone, which are biased towards the massive galaxies in our sample, the slope beyond the transition mass is shallower than for the full sample, indicative of distinct gas processes ongoing for the spirals/high-mass galaxies from other types with lower stellar masses. We then create mock catalogues for the MIGHTEE-H  i detections and non-detections with two main galaxy populations of late- and early-type galaxies to measure the underlying |$M_{\rm H\, {\small {I}}}-M_{\star }$| relation. We find that the turnover in this relation persists whether considering the two galaxy populations as a whole or separately. We note that an underlying linear relation could mimic this turnover in the observed scaling relation, but a model with a turnover is strongly preferred. Measurements on the logarithmic average of H  i masses against the stellar mass are provided as a benchmark for future studies. [ABSTRACT FROM AUTHOR]