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While benchmark dose (BMD) methodology is well-established for settings with a single exposure, these methods cannot easily handle multidimensional exposures with nonlinear effects. We propose a framework for BMD analysis to characterize the joint effect of a two-dimensional exposure on a continuous outcome using a generalized additive model while adjusting for potential confounders via propensity scores. This leads to a dose-response surface which can be summarized in two dimensions by a contour plot in which combinations of exposures leading to the same expected effect are identified. In our motivating study of prenatal alcohol exposure, cognitive deficits in children are found to be associated with both the frequency of drinking as well as the amount of alcohol consumed on each drinking day during pregnancy. The general methodological framework is useful for a broad range of settings, including combinations of environmental stressors, such as chemical mixtures, and in explorations of the impact of dose rate rather than simply cumulative exposure on adverse outcomes., (© 2024 The Authors. Risk Analysis published by Wiley Periodicals LLC on behalf of Society for Risk Analysis.)

A sharing economy is an economy that enables individuals to share their assets with or provide services to those in need through online platforms. Information asymmetry prevents consumers from effectively choosing the best service provider for their needs. Therefore, service providers should offer high-quality signals to address this problem. Unlike other studies, which have focused on platforms for sharing tangible assets (e.g., houses), in this study, we focused on platforms for sharing intangible assets (e.g., knowledge). Specifically, we adopted signaling theory to develop a research model for determining the internal and external signals that service providers should provide on their platforms to attract customers. Public data were collected from the Codementor platform by using Python web scraping. After rigorous data processing, the data obtained from 612 service providers were analyzed to identify key signals. Four crucial internal signals were identified: availability of follower information, availability of reviews, free trial, and service cost. In addition, two crucial external signals were identified: number of projects accomplished and endorsements from other websites related to mentor performance. Overall, our findings expand the application of signaling theory to intangible asset transactions and enable service providers to identify the essential signals that they should provide on their sharing economy platforms to increase the number of consumers interested in their services. [ABSTRACT FROM AUTHOR]

Special measurement effects including the method and testlet effects are common issues in educational and psychological measurement. They are typically covered by various bifactor models or models for the multiple traits multiple methods (MTMM) structure for continuous data and by various testlet effect models for categorical data. However, existing models have some limitations in accommodating different type of effects. With slight modification, the generalized partially confirmatory factor analysis (GPCFA) framework can flexibly accommodate special effects for continuous and categorical cases with added benefits. Various bifactor, MTMM and testlet effect models can be linked to different variants of the revised GPCFA model. Compared to existing approaches, GPCFA offers multidimensionality for both the general and effect factors (or traits) and can address local dependence, mixed-type formats, and missingness jointly. Moreover, the partially confirmatory approach allows for regularization of the loading patterns, resulting in a simpler structure in both the general and special parts. We also provide a subroutine to compute the equivalent effect size. Simulation studies and real-data examples are used to demonstrate the performance and usefulness of the proposed approach under different situations. [ABSTRACT FROM AUTHOR]

The objective of this study is to explore whether four categories of investors overreact or underreact to specific black swan events in the Vietnam Stock Exchange (HSX) and whether their trading patterns can predict stock returns. We find evidence of the short-term overreaction/underreaction on the HSX. In particular, the study's findings indicate that domestic institutions and securities dealers tend to exhibit overreaction shortly after domestic black swan events, such as tensions in the East Sea and the arrest of a big manipulator, but exhibit underreaction shortly after international events, such as the Covid-19 pandemic and the Ukraine-Russia War. Conversely, foreign institutions tend to exhibit overreaction shortly after international events but exhibit underreaction shortly after domestic events. The outcome of this behaviour is that domestic institutions and securities dealers purchased the most and earned the highest return in the post-120-day period following the domestic black swan events, whereas foreign institutions gained the highest return in the post-120-day period following the global black swan events. Moreover, individual investors underreacted in all events and experienced negative returns over the window(+1, + 120). The findings also suggest that small firms tend to have greater price reversals than large firms. Finally, the study provides evidence of information leakage on the HSX. [ABSTRACT FROM AUTHOR]

This paper proposes a comprehensive optimization algorithm for the crossbeam cross-section size to solve the vibration problem of the flexible cable-driven parallel robot frame. The goal is to achieve an innovative design of the cross-sectional structure. By using a differential evolution algorithm, a comprehensive scale analysis of the cross-section size of the beam is carried out. This optimization method effectively improves the cross-section moment of inertia and the bending stiffness of the beam. Additionally, based on the variable density penalty method, the topological optimization design of the cross-section is carried out under the condition of minimum flexibility, the optimization objective function is established, and the objective function is solved by the moving asymptote method. The optimization results show that the beam stiffness is maximized while meeting the minimum flexibility required by the project. Through comparative analysis with various aluminum profile structures and their constructed frames, it is verified that the cross-sectional shape obtained through the optimized design method can significantly improve the anti-vibration stability of the frame. [ABSTRACT FROM AUTHOR]

Diagnostic classification models (DCMs) can be used to track the cognitive learning states of students across multiple time points or over repeated measurements. This study developed an effective variational Bayes (VB) inference method for hidden Markov longitudinal general DCMs. The simulations performed in this study verified the validity of the proposed algorithm for satisfactorily recovering true parameters. Simulation and applied data analyses were conducted to compare the proposed VB method to Markov chain Monte Carlo (MCMC) sampling. The results revealed that the parameter estimates provided by the VB method were consistent with the MCMC method with the additional benefit of a faster estimation time. The comparative simulation also indicated differences between the two methods in terms of posterior standard deviation and coverage of 95% credible intervals. Thus, with limited computational resources and time, the proposed VB method can output estimations comparable to that of MCMC. [ABSTRACT FROM AUTHOR]

Let |$A_{n}$| be the sum of |$d$| permutation matrices of size |$n\times n$|⁠ , each drawn uniformly at random and independently. We prove that the normalized characteristic polynomial |$\frac {1}{\sqrt {d}}\det (I_{n} - z A_{n}/\sqrt {d})$| converges when |$n\to \infty $| towards a random analytic function on the unit disk. As an application, we obtain an elementary proof of the spectral gap of random regular digraphs. Our results are valid both in the regime where |$d$| is fixed and for |$d$| slowly growing with |$n$|⁠. [ABSTRACT FROM AUTHOR]

An intriguing new class of piecewise deterministic Markov processes (PDMPs) has recently been proposed as an alternative to Markov chain Monte Carlo (MCMC). We propose a new class of PDMPs termed Gibbs zig-zag samplers, which allow parameters to be updated in blocks with a zig-zag sampler applied to certain parameters and traditional MCMC-style updates to others. We demonstrate the flexibility of this framework on posterior sampling for logistic models with shrinkage priors for high-dimensional regression and random effects, and provide conditions for geometric ergodicity and the validity of a central limit theorem. [ABSTRACT FROM AUTHOR]

The principles governing genotype-phenotype relationships are still emerging (Jovanovic, Science, 2015, 347 (6,226), 1,259,038; Buccitelli et al., Nature Reviews Genetics, 2020, 21 (10), 630-44; Öztürk et al., Nature Communications, 2022, 131), 6,153), and detailed translational as well as transcriptomic information is required to understand complex phenotypes, such as the pathogenesis of Alzheimer's disease. For this reason, the proteomics of Alzheimer disease (AD) continues to be studied extensively. Although comparisons between data obtained from humans and mouse models have been reported, approaches that specifically address the between-species statistical comparisons are understudied. Our study investigated the performance of two statistical methods for identification of proteins and biological pathways associated with Alzheimer's disease for cross-species comparisons, taking specific data analysis challenges into account, including collinearity, dimensionality reduction and cross-species protein matching. We used a human dataset from a wellcharacterized cohort followed for over 22 years with proteomic data available. For the mouse model, we generated proteomic data from whole brains of CVNAD and matching control mouse models. We used these analyses to determine the reliability of a mouse model to forecast significant proteomic-based pathological changes in the brain that may mimic pathology in human Alzheimer's disease. Compared with LASSO regression, partial least squares discriminant analysis provided better statistical performance for the proteomics analysis. The major biological finding of the study was that extracellular matrix proteins and integrinrelated pathways were dysregulated in both the human and mouse data. This approach may help inform the development of mouse models that are more relevant to the study of human late-onset Alzheimer's disease. [ABSTRACT FROM AUTHOR]

We propose a general method for distributed Bayesian model choice, using the marginal likelihood, where a data set is split in non-overlapping subsets. These subsets are only accessed locally by individual workers and no data is shared between the workers. We approximate the model evidence for the full data set through Monte Carlo sampling from the posterior on every subset generating a model evidence per subset. The results are combined using a novel approach which corrects for the splitting using summary statistics of the generated samples. Our divide-and-conquer approach enables Bayesian model choice in the large data setting, exploiting all available information but limiting communication between workers. We derive theoretical error bounds that quantify the resulting trade-off between computational gain and loss in precision. The embarrassingly parallel nature yields important speed-ups when used on massive data sets as illustrated by our real world experiments. In addition, we show how the suggested approach can be extended to model choice within a reversible jump setting that explores multiple feature combinations within one run. [ABSTRACT FROM AUTHOR]

The stochastic volatility (SV) model and its variants are widely used in the financial sector, while recurrent neural network (RNN) models are successfully used in many large-scale industrial applications of deep learning. We combine these two methods in a nontrivial way and propose a model, which we call the statistical recurrent stochastic volatility (SR-SV) model, to capture the dynamics of stochastic volatility. The proposed model is able to capture complex volatility effects, for example, nonlinearity and long-memory auto-dependence, overlooked by the conventional SV models, is statistically interpretable and has an impressive out-of-sample forecast performance. These properties are carefully discussed and illustrated through extensive simulation studies and applications to five international stock index datasets: the German stock index DAX30, the Hong Kong stock index HSI50, the France market index CAC40, the U.S. stock market index SP500 and the Canada market index TSX250. An user-friendly software package together with the examples reported in the article are available at . [ABSTRACT FROM AUTHOR]

Hilo Bay, Hawai'i, is an estuary of great importance to its neighboring coastal community, but it is threatened by impaired water quality indicated by excessive turbidity and chlorophyll a associated with river discharges of sediments and nutrients. The Wailuku River in the western half of the bay is the primary source of freshwater discharge, hypothesized here to form a surface water-dominant half of the bay with different water quality traits than the groundwater-dominant, eastern half of the bay where the spring-fed Wailoa River discharges. The water quality of both halves of the bay over different flow conditions of the Wailuku River is examined in this study using spatially distributed water quality sampling which collects hundreds of samples in either half of the bay at a distance of about every 40 m. The dense sample shows significant differences between the two halves of the bay, with greater salinity dilution and turbidity in the surface water-dominant area. Both salinity and turbidity have a predictable relation to discharge, with salinity decreasing and turbidity increasing in higher flow conditions. Chlorophyll a, however, has a more complex relation to discharge, as chlorophyll a concentrations are greatest in high-flow conditions, but this may be because the water quality samples were collected in different seasons. Furthermore, significantly greater chlorophyll a concentrations in the groundwater-dominant half of the bay in low-flow conditions show that discharge may be spuriously correlated to chlorophyll a, and further studies of the effects of surface water discharge on chlorophyll a concentrations are warranted. [ABSTRACT FROM AUTHOR]

Water hyacinth (WH) biomass is one of the popular materials in the Vietnamese Mekong Delta, a potential substrate for biogas production. The effectiveness of utilizing WH for producing biogas under anaerobic digestion was demonstrated in the previous studies, but the research was focused on the loading rate of about 1.0% volatile solid (VS). Therefore, in the present study, a semi-continuous anaerobic digestion experiment was conducted with the five levels of VS, including 1.0%VS, 1.5%VS, 2.0%VS, 2.5%VS, and 3.0%VS, to examine how loaded VS can affect biogas production. Each treatment was designed with three replications over 60 days. The measured parameters included pH, temperature (Temp; °C), redox potential (Eh; mV), daily produced biogas volume (L), cumulative biogas volume (L), and methane (CH4) concentration (%) during the 60 days of the experiment. The obtained results showed that pH, tempt, and Eh parameters did not negatively affect biogas production. However, the volume of daily biogas in the treatment of 3.0%VS was higher than in other treatments. In addition, the cumulative biogas volume in the treatment of 3.0%VS was the highest and significantly different between all reactors (p<0.05). Meanwhile, the treatment of 1.0%VS was known with the lowest values. The study explored that the volume of biogas could be increased when the organic loading rate VS is increased. [ABSTRACT FROM AUTHOR]

Generalized linear latent variable models (GLLVMs) are a class of methods for analyzing multi-response data which has gained considerable popularity in recent years, e.g., in the analysis of multivariate abundance data in ecology. One of the main features of GLLVMs is their capacity to handle a variety of responses types, such as (overdispersed) counts, binomial and (semi-)continuous responses, and proportions data. On the other hand, the inclusion of unobserved latent variables poses a major computational challenge, as the resulting marginal likelihood function involves an intractable integral for non-normally distributed responses. This has spurred research into a number of approximation methods to overcome this integral, with a recent and particularly computationally scalable one being that of variational approximations (VA). However, research into the use of VA for GLLVMs has been hampered by the fact that fully closed-form variational lower bounds have only been obtained for certain combinations of response distributions and link functions. In this article, we propose an extended variational approximations (EVA) approach which widens the set of VA-applicable GLLVMs dramatically. EVA draws inspiration from the underlying idea behind the Laplace approximation: by replacing the complete-data likelihood function with its second order Taylor approximation about the mean of the variational distribution, we can obtain a fully closed-form approximation to the marginal likelihood of the GLLVM for any response type and link function. Through simulation studies and an application to a species community of testate amoebae, we demonstrate how EVA results in a “universal” approach to fitting GLLVMs, which remains competitive in terms of estimation and inferential performance relative to both standard VA (where any intractable integrals are either overcome through reparametrization or quadrature) and a Laplace approximation approach, while being computationally more scalable than both methods in practice. [ABSTRACT FROM AUTHOR]

Modern bed bugs are resistant to multiple insecticide classes, particularly the pyrethroids. The efficacy of pyrethroid-impregnated mattress liners marketed for bed bug management has been variable. This study evaluated the efficacy of a permethrin-impregnated mattress liner, ActiveGuard, against 24 bed bug strains, consisting of both Cimex hemipterus (F.) and Cimex lectularius L. A 'mat assay', employing an allethrin-impregnated mat, was used to establish the pyrethroid resistance profile of all strains. Three experiments were conducted to evaluate the effect of ActiveGuard exposure on bed bug knockdown: 1) exposing the bed bugs continuously on the liner for up to 24 d, 2) holding the bed bugs on the liner for either 4 or 6 h, and 3) placing a noninsecticide treated fabric above the liner with the bed bugs held continuously on top. Our results indicated that all modern strains (collected within the last 15 years during the current resurgence) were pyrethroid-resistant, although the magnitude of resistance was highly variable between strains. In the continuous exposure study, an incomplete knockdown was recorded for most modern bed bug strains, with some having no knockdown even up to 7 d of constant exposure. In the 4 or 6 h exposure study, the level of knockdown was reduced even further, and very few bed bugs were knocked down in the double fabric study. The results of this study indicate that pyrethroid-impregnated mattress liners are not likely to be effective in the management of most modern bed bug infestations involving either C. hemipterus or C. lectularius. [ABSTRACT FROM AUTHOR]

Numerous bed bug research papers have been published in the past 20 yr as a result of bed bug (Cimex spp.) (Hemiptera: Cimicidae) resurgence in the world. Yet, few of them focused on the management of the tropical bed bug, C. hemipterus (F.). Here, we describe a case of tropical bed bug infestation in two dormitory buildings and effectiveness of a tropical bed bug treatment program. The study site consisted of 125 dormitories in two buildings. An initial building-wide monitoring with ClimbUp interceptors revealed 25 infestations. The spatial distribution of bed bug infested rooms showed a significant aggregated distribution pattern with same infestation status for neighboring units sharing walls. All infested rooms were monitored every 2 wk and treated using a combination of steam and diatomaceous earth (DE) dust application if bed bugs were still found. For the 25 initially identified infested rooms, after 14 wk treatment, 44% of them no longer had bed bugs, and the mean number of bed bugs captured per room decreased by 94.1%. A combination of steam and DE dust treatment is an effective strategy for suppressing tropical bed bug infestations in dormitory environment. [ABSTRACT FROM AUTHOR]

Background: Most studies of the effects of prenatal alcohol exposure (PAE) on cognitive function have assumed that the dose-response curve is linear. However, data from a few animal and human studies suggest that there may be an inflection point in the dose-response curve above which PAE effects are markedly stronger and that there may be differences associated with pattern of exposure, assessed in terms of alcohol dose per drinking occasion and drinking frequency., Methods: We performed second-order confirmatory factor analysis on data obtained at school age, adolescence, and early adulthood from 2227 participants in six US longitudinal cohorts to derive a composite measure of cognitive function. Regression models were constructed to examine effects of PAE on cognitive function, adjusted for propensity scores. Analyses based on a single predictor (absolute alcohol (AA)/day) were compared with analyses based on two predictors (dose/occasion and drinking frequency), using (1) linear models and (2) nonparametric general additive models (GAM) that allow for both linear and nonlinear effects., Results: The single-predictor GAM model showed virtually no nonlinearity in the effect of AA/day on cognitive function. However, the two-predictor GAM model revealed differential effects of maternal drinking pattern. Among offspring of infrequent drinkers, PAE effects on cognitive function were markedly stronger in those whose mothers drank more than ~3 drinks/occasion, and the effect of dose/occasion was strongest among the very frequent drinkers. Frequency of drinking did not appear to alter the PAE effect on cognitive function among participants born to mothers who limited their drinking to ~1 drink/occasion or less., Conclusions: These findings suggest that linear models based on total AA/day are appropriate for assessing whether PAE affects a given cognitive outcome. However, examination of alcohol dose/occasion and drinking frequency is needed to fully characterize the impact of different levels of alcohol intake on cognitive impairment., (© 2024 Research Society on Alcohol.)

Monte Carlo algorithms, such as Markov chain Monte Carlo (MCMC) and Hamiltonian Monte Carlo (HMC), are routinely used for Bayesian inference; however, these algorithms are prohibitively slow in massive data settings because they require multiple passes through the full data in every iteration. Addressing this problem, we develop a scalable extension of these algorithms using the divide-and-conquer (D&C) technique that divides the data into a sufficiently large number of subsets, draws parameters in parallel on the subsets using a powered likelihood and produces Monte Carlo draws of the parameter by combining parameter draws obtained from each subset. The combined parameter draws play the role of draws from the original sampling algorithm. Our main contributions are twofold. First, we demonstrate through diverse simulated and real data analyses focusing on generalized linear models (GLMs) that our distributed algorithm delivers comparable results as the current state-of-the-art D&C algorithms in terms of statistical accuracy and computational efficiency. Second, providing theoretical support for our empirical observations, we identify regularity assumptions under which the proposed algorithm leads to asymptotically optimal inference. We also provide illustrative examples focusing on normal linear and logistic regressions where parts of our D&C algorithm are analytically tractable. [ABSTRACT FROM AUTHOR]

Summary: We propose a new class of financial volatility models, called the REcurrent Conditional Heteroskedastic (RECH) models, to improve both in‐sample analysis and out‐of‐sample forecasting of the traditional conditional heteroskedastic models. In particular, we incorporate auxiliary deterministic processes, governed by recurrent neural networks, into the conditional variance of the traditional conditional heteroskedastic models, for example, GARCH‐type models, to flexibly capture the dynamics of the underlying volatility. RECH models can detect interesting effects in financial volatility overlooked by the existing conditional heteroskedastic models such as the GARCH, GJR, and EGARCH. The new models often have good out‐of‐sample forecasts while still explaining well the stylized facts of financial volatility by retaining the well‐established features of econometric GARCH‐type models. These properties are illustrated through simulation studies and applications to 31 stock indices and exchange rate data. An user‐friendly software package, together with the examples reported in the paper, is available at https://github.com/vbayeslab. [ABSTRACT FROM AUTHOR]

In order to investigate the distribution and richness of zooplankton on the Wadi M'zi River from November 2018 to October 2019, samples were collected on a monthly basis from four stations. Thirty-three zooplankton species belonging to four taxa have been described from the Wadi M'zi River; Copepoda, Rotifera, Cladocera and Nematoda. The following diversity indexes were used to measure the diversity of zooplankton species in the lake; Shannon Index 3.14 to 3.8, Pielou Evenness Index 0.72 to 0.84 and Margalef Index 1.08 to 1.45. The zooplankton communities were observed to be abundant in spring and summer compared to autumn and winter. Rotifers were found to be dominant followed by copepods, cladocerans and nematods for the entire research duration. CCA analysis indicates that WT, pH, NH4+, NO3-, PO42- and conductivity were responsible for most differences in zooplankton organisms. Finally, we concluded that all sampling area were oligosabrobic indicating the good environmental quality of Wadi M'zi River. [ABSTRACT FROM AUTHOR]

Bayesian computation of high-dimensional linear regression models using Markov chain Monte Carlo (MCMC) or its variants can be extremely slow or completely prohibitive since these methods perform costly computations at each iteration of the sampling chain. Furthermore, this computational cost cannot usually be efficiently divided across a parallel architecture. These problems are aggravated if the data size is large or data arrive sequentially over time (streaming or online settings). This article proposes a novel dynamic feature partitioned regression (DFP) for efficient online inference for high-dimensional linear regressions with large or streaming data. DFP constructs a pseudo posterior density of the parameters at every time point, and quickly updates the pseudo posterior when a new block of data (data shard) arrives. DFP updates the pseudo posterior at every time point suitably and partitions the set of parameters to exploit parallelization for efficient posterior computation. The proposed approach is applied to high-dimensional linear regression models with Gaussian scale mixture priors and spike-and-slab priors on large parameter spaces, along with large data, and is found to yield state-of-the-art inferential performance. The algorithm enjoys theoretical support with pseudoposterior densities over time being arbitrarily close to the full posterior as the data size grows, as shown in the . also contains details of the DFP algorithm applied to different priors. Package to implement DFP is available in . The dataset is available in https://github.com/Rene-Gutierrez/DynParRegReg\_Implementation. [ABSTRACT FROM AUTHOR]

Posterior Predictive Model Checking (PPMC) is frequently used for model fit evaluation in Bayesian Confirmatory Factor Analysis (BCFA). In standard PPMC procedures, model misfit is quantified by comparing the location of an ML-based point estimate to the predictive distribution of a statistic. When the point estimate is far from the center posterior predictive distribution, model fit is poor. Not included in this approach, however, is the variability of the Maximum Likelihood (ML)-based point estimates. We propose a new method of PPMC based on comparing posterior predictive distributions of a hypothesized and saturated BCFA model. The method uses the predictive distribution of the saturated model as a reference and the Kolmogorov-Smirnov (KS) statistic to quantify the local misfit of hypothesized models. The results of the simulation study suggest that the saturated model PPMC approach was an accurate method of determining local model misfit and could be used for model comparison. A real data example is also provided in this study. [ABSTRACT FROM AUTHOR]

The goal of this article is to provide a way for Bayesian statisticians to incorporate subsampling directly into the Bayesian hierarchical model of their choosing without imposing additional restrictive model assumptions. We are motivated by the fact that the rise of "big data" has created difficulties for statisticians to directly apply their methods to big datasets. We introduce a "data subset model" to the popular "data model, process model, and parameter model" framework used to summarize Bayesian hierarchical models. The hyperparameters of the data subset model are specified constructively in that they are chosen such that the implied size of the subset satisfies predefined computational constraints. Thus, these hyperparameters effectively calibrate the statistical model to the computer itself to obtain predictions/estimations in a prespecified amount of time. Several properties of the data subset model are provided including: propriety, partial sufficiency, and semi-parametric properties. Simulated datasets will be used to assess the consequences of subsampling, and results will be presented across different computers to show the effect of the computer on the statistical analysis. Additionally, we provide a joint analysis of a high-dimensional dataset (roughly 10 gigabytes) consisting of 2018 5-year period estimates from the U.S. Census Bureau's Public Use Micro-Sample (PUMS). [ABSTRACT FROM AUTHOR]

Speeding up Markov chain Monte Carlo (MCMC) for datasets with many observations by data subsampling has recently received considerable attention. A pseudo-marginal MCMC method is proposed that estimates the likelihood by data subsampling using a block-Poisson estimator. The estimator is a product of Poisson estimators, allowing us to update a single block of subsample indicators in each MCMC iteration so that a desired correlation is achieved between the logs of successive likelihood estimates. This is important since pseudo-marginal MCMC with positively correlated likelihood estimates can use substantially smaller subsamples without adversely affecting the sampling efficiency. The block-Poisson estimator is unbiased but not necessarily positive, so the algorithm runs the MCMC on the absolute value of the likelihood estimator and uses an importance sampling correction to obtain consistent estimates of the posterior mean of any function of the parameters. Our article derives guidelines to select the optimal tuning parameters for our method and shows that it compares very favorably to regular MCMC without subsampling, and to two other recently proposed exact subsampling approaches in the literature. for this article are available online. [ABSTRACT FROM AUTHOR]

Sequential Monte Carlo (SMC) samplers are an alternative to MCMC for Bayesian computation. However, their performance depends strongly on the Markov kernels used to rejuvenate particles. We discuss how to calibrate automatically (using the current particles) Hamiltonian Monte Carlo kernels within SMC. To do so, we build upon the adaptive SMC approach of Fearnhead and Taylor (2013), and we also suggest alternative methods. We illustrate the advantages of using HMC kernels within an SMC sampler via an extensive numerical study. [ABSTRACT FROM AUTHOR]

Linear mixed-effects models play a fundamental role in statistical methodology. A variety of Markov chain Monte Carlo (MCMC) algorithms exist for fitting these models, but they are inefficient in massive data settings because every iteration of any such MCMC algorithm passes through the full data. Many divide-and-conquer methods have been proposed to solve this problem, but they lack theoretical guarantees, impose restrictive assumptions, or have complex computational algorithms. Our focus is one such method called the Wasserstein Posterior (WASP), which has become popular due to its optimal theoretical properties under general assumptions. Unfortunately, practical implementation of the WASP either requires solving a complex linear program or is limited to one-dimensional parameters. The former method is inefficient and the latter method fails to capture the joint posterior dependence structure of multivariate parameters. We develop a new algorithm for computing the WASP of multivariate parameters that is easy to implement and is useful for computing the WASP in any model where the posterior distribution of parameter belongs to a location-scatter family of probability measures. The algorithm is introduced for linear mixed-effects models with both implementation details and theoretical properties. Our algorithm outperforms the current state-of-the-art method in inference on the functions of the covariance matrix of the random effects across diverse numerical comparisons. Supplemental materials for this article are available online. [ABSTRACT FROM AUTHOR]

We show how to speed up sequential Monte Carlo (SMC) for Bayesian inference in large data problems by data subsampling. SMC sequentially updates a cloud of particles through a sequence of distributions, beginning with a distribution that is easy to sample from such as the prior and ending with the posterior distribution. Each update of the particle cloud consists of three steps: reweighting, resampling, and moving. In the move step, each particle is moved using a Markov kernel; this is typically the most computationally expensive part, particularly when the dataset is large. It is crucial to have an efficient move step to ensure particle diversity. Our article makes two important contributions. First, in order to speed up the SMC computation, we use an approximately unbiased and efficient annealed likelihood estimator based on data subsampling. The subsampling approach is more memory efficient than the corresponding full data SMC, which is an advantage for parallel computation. Second, we use a Metropolis within Gibbs kernel with two conditional updates. A Hamiltonian Monte Carlo update makes distant moves for the model parameters, and a block pseudo-marginal proposal is used for the particles corresponding to the auxiliary variables for the data subsampling. We demonstrate both the usefulness and limitations of the methodology for estimating four generalized linear models and a generalized additive model with large datasets. [ABSTRACT FROM AUTHOR]

Historically, in civil engineering construction, the structural design takes account the gravity, overload and water pressure as fundamental forces. Becomes later the consideration of the dynamics effects as the machine vibration and the earthquake, the fire resistance...etc. After all, the survey of projects like building, road, pavement -as example- show that there is always appearance of various anomalies and damages that are evolutionary in time. Often, they can be due to the water cycle. The paper handles the hydro mechanical coupling case, shows how conducting the analysis and proof the feasibility of the structural design in these conditions. The results are obtained in the frame of the finite element analysis and they open the horizon for more improvements. [ABSTRACT FROM AUTHOR]

This study examined the presence of insecticide resistance in different developmental stages (adults, first instars, and eggs) of the tropical bed bug, Cimex hemipterus (F.) using several insecticide formulations. Adults and first instars of five strains (Queensland, Kuala Lumpur, Bukit Mertajam, Saujana, and Krystal Point) were evaluated using the surface contact method and compared with a susceptible strain (Monheim) of the common bed bug Cimex lectularius L. The insecticide formulations were used at their label rates in this study: Tandem (thiamethoxam [11.6%], lambda-cyhalothrin [3.5%]) at 183.96 mg/m2; Temprid SC (imidacloprid [21%], beta-cyfluthrin [10.5%]) at 106.13 mg/m2; Sumithion 20CS (fenitrothion [20%]) at 250 mg/m2; Pesguard FG161 (d-tetramethrin [4.4%], cyphenothrin [13.2%]) at 110 mg/m2; and Sumithrin 10SEC (d-phenothrin [10%]) at 100 mg/m2. Results showed a very high level of resistance to Pesguard FG161 (388.3 to >605.0 times) and Sumithrin (302.9 to >365.5 times) in all adults of the strains tested, whereas low to high levels of resistance were registered for Tandem (1.4–4.7 times), Temprid (7.3–16.7 times), and Sumithion (1.2–14.6 times) for adults of all bed bug strains. For first instars, resistance to the former two formulations were high to very high (31.4–118.1 times). In contrast, they showed lower resistance to Tandem, Temprid, and Sumithion (1.0–10.2 times). An immersion method used to test on bed bug eggs found high to very high resistance toward all tested formulations. Results demonstrate that the resistance level varies between bed bug developmental stages. [ABSTRACT FROM AUTHOR]

Livestock production plays a leading role in agricultural land-use change. Producing biogas from livestock waste and subsequently using the biogas effluent as fertilizer for crops is a promising option to solve environmental problems resulting from expanding livestock production. However, it is difficult to promptly and accurately measure the nitrogen (N) concentration of effluent for farmers in developing countries, making precise N management difficult. The objectives of the current study were (1) to evaluate the feasibility of variable-timing, fixed-rate application of cattle biogas effluent using a leaf color chart (LCC) for rice (Oryza sativa L.) and (2) to determine the optimum LCC threshold for grain yield. We conducted two microcosm experiments in the Mekong Delta of Vietnam in 2018 using eight treatments of N-fertilizer application. In the Zero treatment, we applied no N. In the Estd treatment, we split-applied N as effluent (E) at fixed rate and timing as the standard method. In E2.75, E3.00, E3.25, E3.50, and E3.75, we applied effluent whenever the LCC value went below 2.75, 3.00, 3.25, 3.50, and 3.75, respectively. In U3.25, we applied N as urea (U) whenever the LCC value fell below 3.25. The total effluent-N application rate ranged from 90 to 210 kg N ha−1 season−1. Rice growth was normal but there was a substantial yield gap between the two microcosm experiments due to the seasonal difference in solar radiation. Rice yield tended to increase with increasing LCC threshold. There was a positive linear relationship between LCC and chlorophyll content (SPAD) values (R2 = 0.73–0.79). Grain yield was well explained (R2 = 0.70–0.89) by the seasonal mean LCC or SPAD value. Plant total N uptake increased with increasing LCC threshold, but the three calculated indices of N use efficiency (NUE) – apparent N recovery, agronomic NUE, and internal NUE – were not always improved with a higher LCC threshold. Our results showed that the tested variable-timing, fixed-rate strategy for the application of cattle biogas effluent was feasible and the optimum LCC threshold for grain production was 3.75 under the current microcosm conditions. [ABSTRACT FROM AUTHOR]

We describe an "embarrassingly parallel" method for Bayesian phylogenetic inference, annealed Sequential Monte Carlo (SMC), based on recent advances in the SMC literature such as adaptive determination of annealing parameters. The algorithm provides an approximate posterior distribution over trees and evolutionary parameters as well as an unbiased estimator for the marginal likelihood. This unbiasedness property can be used for the purpose of testing the correctness of posterior simulation software. We evaluate the performance of phylogenetic annealed SMC by reviewing and comparing with other computational Bayesian phylogenetic methods, in particular, different marginal likelihood estimation methods. Unlike previous SMC methods in phylogenetics, our annealed method can utilize standard Markov chain Monte Carlo (MCMC) tree moves and hence benefit from the large inventory of such moves available in the literature. Consequently, the annealed SMC method should be relatively easy to incorporate into existing phylogenetic software packages based on MCMC algorithms. We illustrate our method using simulation studies and real data analysis. [ABSTRACT FROM AUTHOR]

The common bed bug Cimex lectularius L. has undergone a worldwide expansion in recent years, due to increased opportunities for dispersal and development of insecticide resistance. For successful control, efficacy testing of products against bed bugs and determination of insecticide resistance under practical conditions are of outstanding importance. A new test system mimicking the practical use situation of residual insecticides was developed and evaluated. Bed bugs were attracted by CO2 and heat to cross surfaces treated with alpha-cypermethrin and bendiocarb. In contrast to the complete efficacy of alpha-cypermethrin (less than 1% surviving bed bugs [with one exception of 5%]), only 45.3, 46, and 29% of insecticide-susceptible bed bugs showed lethal damage 7 d after contact with freshly bendiocarb-treated wallpaper or insecticide aged for 1 or 2 wk. Results show that the efficacy of different insecticides can be assessed with this new test system. Moreover, susceptibility to deltamethrin of five bed bug field strains, collected from infested apartments in Berlin, Germany, was determined in a filter paper contact bioassay. Resistance ratios (RRs) ranged between 4.3 and 20.7. In the novel simulated-use test, efficacy of alpha-cypermethrin was tested against the bed bug strain with the highest RR. In contrast to the insecticide-susceptible laboratory strain, alpha-cypermethrin was not effective against the field strain, where 26–50% of the bed bugs survived and even laid eggs. These results provide evidence for the presence of practically relevant pyrethroid resistance in bed bugs in Germany. [ABSTRACT FROM AUTHOR]

We propose subsampling Markov chain Monte Carlo (MCMC), an MCMC framework where the likelihood function for n observations is estimated from a random subset of m observations. We introduce a highly efficient unbiased estimator of the log-likelihood based on control variates, such that the computing cost is much smaller than that of the full log-likelihood in standard MCMC. The likelihood estimate is bias-corrected and used in two dependent pseudo-marginal algorithms to sample from a perturbed posterior, for which we derive the asymptotic error with respect to n and m, respectively. We propose a practical estimator of the error and show that the error is negligible even for a very small m in our applications. We demonstrate that subsampling MCMC is substantially more efficient than standard MCMC in terms of sampling efficiency for a given computational budget, and that it outperforms other subsampling methods for MCMC proposed in the literature. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

Background and aims - Biomonitoring is an important tool for assessing river water quality, but is not routinely applied in tropical rivers. Marked hydrological changes can occur between wet and dry season conditions in the tropics. Thus, a prerequisite for ecological assessment is that the influence of 'natural' hydrological change on biota can be distinguished from variability driven by water quality parameters of interest. Here we aimed to (a) assess seasonal changes in water quality, diatoms and algal assemblages from river phytoplankton and artificial substrates through the dry-wet season transition (February-July 2018) in the Red River close to Hanoi and (b) evaluate the potential for microscopic counts and high-performance liquid chromatography (HPLC) analysis of chlorophyll and carotenoid pigments for biomonitoring in large tropical rivers. Methods - River water (phytoplankton) and biofilms grown on artificial glass substrates were sampled monthly through the dry (February-April) to wet (May-August) season transition and analysed via microscopic and HPLC techniques. Key results - All phototrophic communities shifted markedly between the dry and wet seasons. Phytoplankton concentrations were low (c. thousands of cells/mL) and declined as the wet season progressed. The dominant phytoplankton taxa were centric diatoms (Aulacoseira granulata and Aulacoseira distans) and chlorophytes (Scenedesmus and Pediastrum spp.), with chlorophytes becoming more dominant in the wet season. Biofilm diatoms were dominated by Melosira varians, and areal densities declined in the wet season when fast-growing pioneer diatom taxa (e.g. Achnanthidium minutissimum, Planothidium lanceolatum) and non-degraded Chlorophyll a concentrations increased, suggesting active phytobenthos growth in response to scour damage. Otherwise, a-phorbins were very abundant in river seston and biofilms indicating in situ Chlorophyll a degradation which may be typical of tropical river environments. The very large range of total suspended solids (reaching > 120 mg/L) and turbidity appears to be a key driver of photoautotrophs through control of light availability. Conclusions - Hydrological change and associated turbidity conditions exceed nutrient influences on photoautotrophs at inter-seasonal scales in this part of the Red River. Inter-seasonal differences might be a useful measure for biomonitoring to help track how changes in suspended solids, a major water quality issue in tropical rivers, interact with other variables of interest. [ABSTRACT FROM AUTHOR]

The rapid development of computing power and efficient Markov Chain Monte Carlo (MCMC) simulation algorithms have revolutionized Bayesian statistics, making it a highly practical inference method in applied work. However, MCMC algorithms tend to be computationally demanding, and are particularly slow for large datasets. Data subsampling has recently been suggested as a way to make MCMC methods scalable on massively large data, utilizing efficient sampling schemes and estimators from the survey sampling literature. These developments tend to be unknown by many survey statisticians who traditionally work with non-Bayesian methods, and rarely use MCMC. Our article explains the idea of data subsampling in MCMC by reviewing one strand of work, Subsampling MCMC, a so called Pseudo-Marginal MCMC approach to speeding up MCMC through data subsampling. The review is written for a survey statistician without previous knowledge of MCMC methods since our aim is to motivate survey sampling experts to contribute to the growing Subsampling MCMC literature. [ABSTRACT FROM AUTHOR]

The monitoring and assessment of surface water properties has been mainly conducted for freshwater rivers using multivariate analyses. The present study aimed to evaluate the effectiveness of introducing the absorption spectrum of water in order to develop a better monitoring and assessment technique for the surface waters of a river-estuary-sea continuum. Fourteen variables were measured at 17 different sites along three rivers, including estuaries and coastal sea, in Brunei Darussalam, Borneo, from August 2017 to December 2020. Among the 17 sites, freshwater streams surrounded by primary forests and coastal seawaters had the lowest ranges of the absorption coefficient at 350 nm, suspended solids, and nutrients, but had the highest range of dissolved oxygen concentration. The other sites were more or less affected by natural leaching of chromophoric dissolved organic matter (CDOM) from forests, soil runoff from agricultural lands, and wastewater discharge from urban and industrial activities. The principal component analysis (PCA) showed that four principal components were significant and explained 91% of the total variance in the data set. Four most explanatory factors were also extracted using the factor analysis (FA). In addition to the absorption spectrum, only seven basic water variables (salinity, pH, dissolved oxygen, total suspended solid, particulate organic matter, particulate inorganic matter, phosphate) were found to be necessary to assess the spatial variations in water properties at the present river-estuary-sea continuum. We conclude that it is important to include absorption spectrum data in water monitoring programs for more efficient and comprehensive assessment at land-sea interfaces., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

The performance of five insecticides (bendiocarb, deltamethrin, DDT, malathion, and imidacloprid) using three application methods (oil-based insecticide films on filter paper, and acetone-based insecticide deposits on two substrates: filter paper and glass) was assessed against a susceptible strain of Cimex lectularius (L.) and two resistant strains of Cimex hemipterus (F.). Substrate type significantly affected (P<0.05) the insecticide knockdown response of the susceptible strain in acetone-based insecticide bioassays, with longer survival time on filter paper than on the glass surface. With the exception of deltamethrin, the different diluents (oil and acetone) also significantly affected (P<0.05) the insecticide knockdown response of the susceptible strain in the filter paper-based insecticide bioassays, with longer survival time with acetone as the diluent. For both strains of C. hemipterus, there were no significant effects with the different surfaces and diluents for all insecticides except for malathion and imidacloprid, which was largely due to high levels of resistance. The lower effectiveness for the insecticide acetone-based treatment on filter paper may be due to crystal bloom. This occurs when an insecticide, dissolved in a volatile solvent, is applied onto absorptive surfaces. The effect is reduced on nonabsorptive surfaces and slowed down with oil-based insecticides, whereby the oil forms a film on absorptive surfaces. These findings suggest that nonabsorptive surfaces should be used in bioassays to monitor insecticide resistance. If absorptive surfaces are used in bioassays for testing active ingredients, then oil-based insecticides should be preferably used. [ABSTRACT FROM AUTHOR]

Resistance to insecticides, especially the pyrethroids, in the common bed bug, Cimex lectularius L., has been well-documented. However, the presence and relative contribution of metabolic detoxifying microsomal oxidases and hydrolytic esterases to the observed resistance has yet to be fully elucidated. This is due, in part, to the absence of a simple bioassay procedure that appropriately isolates esterases from potentially competing oxidases. Recently, an analogue of piperonyl butoxide (PBO) was developed, EN16/5-1 (6-[2-(2-butoxyethoxy) ethoxymethyl]-5-propyl-2,3-dihydrobenzofuranby), which inhibits esterases but has limited efficacy against the oxidases, whereas PBO inhibits both. The opportunity is now available to use both synergists via established bioassay methodologies and to screen for the potential presence of oxidase- or esterase-derived pyrethroid resistance in insecticide-resistant insects, including bed bugs. In the present study, EN16/5-1 and PBO were assayed in conjunction with deltamethrin against four field strains of C. lectularius collected from independent geographic locations across Australia. All strains expressed a high degree of resistance to deltamethrin and significant inhibition of the observed resistance with preexposure to PBO. Nonsignificant differences between the cumulative mortality values for PBO and EN16/5-1 were then observed in two of the four bed bug strains, which indicate that detoxifying esterases are conferring substantially to the observed resistance in those strains. This study is the first to provide evidence that metabolic detoxification in the form of both hydrolytic esterases and microsomal oxidases is a major contributing factor to pyrethroid resistance in C. lectularius. [ABSTRACT FROM AUTHOR]