Bayesian Inference Approach Research Articles

Using spherical indentation to determine creep behavior with considering empirical friction coefficient

Indentation testing is a common technique for characterizing the mechanical properties of materials. When it comes to assessing the yield behavior of metals, conical indentation is often favored due to its ability to induce significant plastic deformation at relatively shallow indentation depths. On the other hand, spherical indentation is typically chosen for evaluating metal creep behavior, as it helps minimize the influence of plastic deformation on creep measurements. In spherical indentation tests, the friction at the contact interface is particularly crucial, given the larger contact area and more pronounced slip zones. However, accurately determining the friction coefficient at the contact interface is a complex multi-scale challenge, with the exact coefficient varying based on specific testing conditions and surface treatments. Consequently, empirical friction coefficients are frequently employed, often without thorough justification. In this work, we proposed Bayesian inference method for obtaining the creep constitutive behavior of alloys through spherical indentation tests. Our model considers not only the complexity of creep law, but also the indentation friction, which is unable to consider in most spherical indentation tests with traditional treatment. By using experimental data of spherical indentation tests on 310S stainless steel and pure nickel alloy from literature, present study demonstrates the effectiveness of the Bayesian inference approach, with the inferred constitutive models exhibiting well agreement with uniaxial creep behavior. Furthermore, the method considers the friction coefficient as an extra factor in inferring creep constitutive behavior from indentation tests.

Complete mitochondrial genome of Guigarracailaoensis Wang, Chen & Zheng, 2022 (Cypriniformes, Cyprinidae) and its phylogenetic implications.

Guigarracailaoensis is a member of family Cyprinidae, subfamily Labeoninae (Cypriniformes) which was recently discovered in southwestern China. Following its initial description, additional information on this species has remained notably scarce. In the current study, we assemble the complete mitochondrial genome (mitogenome) of G.cailaoensis using the Illumina sequencing platform. The mitogenome is identified as a circular, double-stranded DNA sequence of 16,593 base pairs, encompassing 13 protein-coding genes (PCGs), 22 transfer RNA genes, two ribosomal RNA genes, and a putative control region. Maximum-likelihood and Bayesian-inference approaches were used to construct phylogenetic trees for three datasets: (i) PCG sequences of the complete mitogenome (dataset 1); (ii) PCG sequences of the complete mitogenome combined with nuclear DNA (ncDNA) (Rag1) sequence (dataset 2); and (iii) ncDNA (Rag1) sequences (dataset 3). Phylogenetic analyses position G.cailaoensis as a sister taxon to the lineage consisting of Paraqianlabeolineatus Zhao, Sullivan, Zhang & Peng, 2014 and Pseudogyrinocheilusprochilus Fang, 1933 in dataset 1, and to Pseudogyrinocheilusprochilus in dataset 2, species lacking an oral disc on the lower lip. However, G.cailaoensis showed a close relationship to the lineage consisting of Discogobio and Discocheilus in dataset 3, species possessing an oral disc on the lower lip. Nonetheless, a variety of species with an oral disc on the lower lip are clustered into different lineages across the three datasets that may indicate that the development of the oral disc is homoplastic within the subfamily Labeoninae. The outcomes of this study have the potential to support conservation efforts for this species and to enrich our understanding of genetic resources in the area.

Four complete mitochondrial genomes of the subgenus Pterelachisus (Diptera, Tipulidae, Tipula) and implications for the higher phylogeny of the family Tipulidae.

The complete mitochondrial genomes of Tipula (Pterelachisus) cinereocincta mesacantha Alexander, 1934, T. (P.) legalis Alexander, 1933, T. (P.) varipennis Meigen, 1818, and T. (P.) yasumatsuana Alexander, 1954 are reported, three of them being sequenced for the first time. The mitochondrial genome lengths of the four species are 15,907 bp, 15,625 bp, 15,772 bp, and 15,735 bp, respectively. All genomes exhibit a high AT base composition, with A + T content of 76.7%, 75.0%, 77.8%, and 75.4%, respectively. The newly reported mitogenomes herein show a general similarity in overall structure, gene order, base composition, and nucleotide content to those of the previously studied species within the family Tipulidae. Phylogenetic analyses were conducted to investigate the relationships within Tipulidae, using both Maximum Likelihood and Bayesian Inference approaches. The results show that the four target species of the subgenus T. (Pterelachisus) basically form a monophyletic group within Tipulidae, clustering with species of the Tipula subgenera T. (Lunatipula), T. (Vestiplex), and T. (Formotipula); however, the genus Tipula is not monophyletic. Moreover, neither the tipulid subfamily Tipulinae nor the family Limoniidae is supported to be a monophyletic group. The monophyly of the family Tipulidae, and the sister relationship between Tipulidae and Cylindrotomidae are reconfirmed. These research findings could contribute to deep insights into the systematic and evolutionary patterns of crane flies.

Data assimilation of decay heat experiments for feedback on nuclear data

Integral decay heat experiments can provide interesting feedback on particular nuclear data (decay data and fission yields mainly). After ensuring that the C/E discrepancies were mostly due to nuclear data discrepancies, a Bayesian inference approach can be applied. Nevertheless, the results strongly depend on the quality of the experiment and on our capability to estimate realistic experimental correlation matrices when considering several integral experiments in the assimilation process. A former study performed in 2019 was dedicated to the data assimilation of a large C/E dataset from the experimental validation database of fuel inventory calculations with the DARWIN2.3 package in order to provide feedback to the nuclear data evaluators. This paper is an attempt to exploit the General Electric decay heat experiments performed in the USA in the 1980s in order to confirm or not the trends on four particular cumulated fission yields: 235U(nth,f)133Cs, 235U(nth,f)137Cs, 239Pu(nth,f)106Ru and 239Pu(nth,f)144Ce.

Multi-domain Adversarial Variational Bayesian Inference for Domain Generalization

Domain generalization aims to learn common knowledge from multiple observed source domains and transfer it to unseen target domains, e.g. the object recognition in varieties of visual environments. Traditional domain generalization methods aim to learn the feature representation of the raw data with its distribution invariant across domains. This relies on the assumption that the two posterior distributions (the distributions of the label given the feature distribution and given the raw data) are stable in different domains. However, this does not always hold in many practical situations. In this paper, we relax the above assumption by permitting the posterior distribution of the label given the raw data changes in difference domains, and thus focuses on a more realistic learning problem that infers the conditional domain-invariant feature representation. Specifically, a multi-domain adversarial variational Bayesian inference approach is proposed to minimize the inter-domain discrepancy of the conditional distributions of the feature given the label. Besides, it is imposed by the constraints from the adversarial learning and feedback mechanism to enhance the condition invariant feature representation. The extensive experiments on two datasets demonstrate the effectiveness of our approach, as well as the state-of-the-art performance comparing with thirteen methods.

Statistical Analysis of Core Strength of Deep Cement Mixing Ground

Cement-treated ground by deep cement mixing has been used for several geotechnical structures widely. In quality assurance processes of this method, statistical parameters, mean and standard deviation, of unconfined compressive strength of cement-treated soils are used. The mean and standard deviation are normally used to assure the quality of the improved ground. These parameters evaluated from core strength data are the sample statistical parameters, indicating these parameters involve the statistical uncertainty. Thus the evaluation of the statistical uncertainty is needed when assuring the quality of the improved ground precisely. Moreover, the spatial correlation exists in core strength data. The statistical uncertainty emerging in the evaluation of the population statistical parameters is possibly affected by the spatial correlation. This paper presents the statistical analysis of core strength data observed in several deep cement mixing projects. The mean, standard deviation, and autocorrelation distance, were adopted as the statistical parameters of the strength. The type of the probability distribution of the core strength was investigated by the Kolmogoronv-Smirnov (K-S) test. The goodness fit of the normal and log-normal distributions was examined against the core strength data. The autocorrelation distance, which is the parameter representing the characteristic of the spatial correlation, was calculated from the distribution of the core strength using the maximum-likelihood method. The statistical uncertainty of the statistical parameters was evaluated using a Bayesian inference approach. In the Bayesian inference approach, a Markov chain Monte Carlo method was adopted to calculate the realizations of the population statistical parameters. The analysis results indicated the statistical uncertainty included in the statistical parameters is significantly affected by the spatial correlation.

A Model-Based Bayesian Inference Approach for On-Board Monitoring of Rail Roughness Profiles: Application on Field Measurement Data of the Swiss Federal Railways Network

According to the International Union of Railways, railway networks count more than one million kilometers of tracks worldwide, a number that is to rise further as the goal is to promote rail transportation as a sustainable means to face the challenge of increased mobility. However, such a vast expansion further necessitates efficient and reliable infrastructure monitoring schemes able to guarantee the quality and safety of rail transportation. Traditional monitoring approaches, relying on visual inspection and portable measuring devices, cannot rise to the task as they do not allow for continuous inspection of extended portions of rail infrastructure. Therefore, mobile monitoring methodologies based on dedicated diagnostic vehicles have emerged as an alternative. Despite revolutionizing traditional monitoring methods, such vehicles are usually expensive and can only operate under the suspension of regular rail service. In this work, we propose an alternative approach for mobile sensing of railway infrastructure based on on-board monitoring data collected from low-cost vibration sensors, e.g., accelerometers, which can be mounted on in-service trains. Specifically, we focus on identifying the roughness profile of the tracks and propose a fusion of reduced-order vehicle models with a Bayesian inference approach for joint input-state estimation. To enhance the inference, we opt for a prior updating of the vehicle model parameters on the basis of an unscented Kalman filter and available measurements from a diagnostic vehicle. The key contributions of this work are (i) the consideration of the dynamic interaction between trains and tracks, which is usually ignored in rail roughness estimation, (ii) the adoption of reduced train vehicle models that decrease the computational effort of the identification task, (iii) the updating of the vehicle parameters to account for inconsistencies in the model used, and (iv) the application of the proposed methodology to actual acceleration measurements collected from a diagnostic vehicle of the Swiss Federal Railways network.

Resolution-independent generative models based on operator learning for physics-constrained Bayesian inverse problems

The Bayesian inference approach is widely used to solve inverse problems due to its versatile and natural ability to handle ill-posedness. However, there are often challenges when dealing with situations involving continuous fields or parameters with large-resolution discrete representations. Furthermore, the prior distribution of the unknown parameters is also commonly difficult to determine. Therefore, in this study, an operator learning-based generative adversarial network (OL-GAN) is proposed and integrated into the Bayesian inference framework to address these issues. Compared with classical Bayesian approaches, the distinctive characteristic of the proposed method is that it learns the joint distributions of parameters and responses. By using the trained generative model to handle the prior in Bayes’ rule, the posteriors of the unknown parameters can theoretically be approximated by any sampling algorithms (e.g., Markov chain Monte Carlo, MCMC) under the proposed framework. More importantly, efficient sampling can be implemented in a low-dimensional latent space shared by the components of the joint distribution. The latent space is typically a simple and easy-to-sample distribution (e.g., Gaussian, uniform), which significantly reduces the computational cost associated with the Bayesian inference while avoiding prior selection. Furthermore, the generator is resolution-independent due to the incorporation of operator learning. Predictions can thus be obtained at desired coordinates, and inversions can be performed even if the observation data are misaligned with the training data. Finally, the effectiveness of the proposed method is validated through several numerical experiments.

Inference for entomological semi-field experiments: Fitting a mathematical model assessing personal and community protection of vector-control interventions

The effectiveness of vector-control tools is often assessed by experiments as a reduction in mosquito landings using human landing catches (HLCs). However, HLCs alone only quantify a single characteristic and therefore do not provide information on the overall impacts of the intervention product. Using data from a recent semi-field study which used time-stratified HLCs, aspiration of non-landing mosquitoes, and blood feeding, we suggest a Bayesian inference approach for fitting such data to a stochastic model. This model considers both personal protection, through a reduction in biting, and community protection, from mosquito mortality and disarming (prolonged inhibition of blood feeding). Parameter estimates are then used to predict the reduction of vectorial capacity induced by etofenpox-treated clothing, picaridin topical repellents, transfluthrin spatial repellents and metofluthrin spatial repellents, as well as combined interventions for Plasmodium falciparum malaria in Anopleles minimus. Overall, all interventions had both personal and community effects, preventing biting and killing or disarming mosquitoes. This led to large estimated reductions in the vectorial capacity, with substantial impact even at low coverage. As the interventions aged, fewer mosquitoes were killed; however the impact of some interventions changed from killing to disarming mosquitoes. Overall, this inference method allows for additional modes of action, rather than just reduction in biting, to be parameterised and highlights the tools assessed as promising malaria interventions.

Mathematical Modeling Identifies Optimum Palbociclib-fulvestrant Dose Administration Schedules for the Treatment of Patients with Estrogen Receptor-positive Breast Cancer.

We created a computational modeling platform to predict the effects of fulvestrant/palbocilib treatment on WT-ER and Y537S-mutant breast cancer cells, and found that continuous treatment schedules are more effective than the standard, pulsed-dose palbociclib treatment schedule.

Hierarchical Bayesian Inference of Globular Cluster Properties

Abstract We present a hierarchical Bayesian inference approach to estimating the structural properties and the phase space center of a globular cluster (GC) given the spatial and kinematic information of its stars based on lowered isothermal cluster models. As a first step towards more realistic modelling of GCs, we built a differentiable, accurate emulator of the lowered isothermal distribution function using interpolation. The reliable gradient information provided by the emulator allows the use of Hamiltonian Monte Carlo methods to sample large Bayesian models with hundreds of parameters, thereby enabling inference on hierarchical models. We explore the use of hierarchical Bayesian modelling to address several issues encountered in observations of GC including an unknown GC center, incomplete data, and measurement errors. Our approach not only avoids the common technique of radial binning but also incorporates the aforementioned uncertainties in a robust and statistically consistent way. Through demonstrating the reliability of our hierarchical Bayesian model on simulations, our work lays out the foundation for more realistic and complex modelling of real GC data.

Fast Bayesian gravitational wave parameter estimation using convolutional neural networks

ABSTRACT The determination of the physical parameters of gravitational wave events is a fundamental pillar in the analysis of the signals observed by the current ground-based interferometers. Typically, this is done using Bayesian inference approaches which, albeit very accurate, are very computationally expensive. We propose a convolutional neural network approach to perform this task. The convolutional neural network is trained using simulated signals injected in a Gaussian noise. We verify the correctness of the neural network’s output distribution and compare its estimates with the posterior distributions obtained from traditional Bayesian inference methods for some real events. The results demonstrate the ability of the convolutional neural network to produce posterior distributions that are compatible with the traditional methods. Moreover, it achieves a remarkable inference speed, lowering by orders of magnitude the times of Bayesian inference methods, enabling real-time analysis of gravitational wave signals. Despite the observed reduced accuracy in the parameters, the neural network provides valuable initial indications of key parameters of the event such as the sky location, facilitating a multimessenger approach.

Whole genome sequencing of outbreak strains from 2017 to 2018 reveals an endemic clade of dengue 1 virus in Cameroon

ABSTRACT Dengue fever is expanding as a global public health threat including countries within Africa. For the past few decades, Cameroon has experienced sporadic cases of arboviral infections including dengue fever. Here, we conducted genomic analyses to investigate the origin and phylogenetic profile of Cameroon DENV-1 outbreak strains and predict the impact of emerging therapeutics on these strains. Bayesian and maximum-likelihood phylogenetic inference approaches were employed in virus evolutionary analyses. An in silico analysis was performed to assess the divergence in immunotherapeutic and vaccine targets in the new genomes. Six complete DENV-1 genomes were generated from 50 samples that met a clinical definition for DENV infection. Phylogenetic analyses revealed that the strains from the current study belong to a sub-lineage of DENV-1 genotype V and form a monophyletic taxon with a 2012 strain from Gabon. The most recent common ancestor (TMRCA) of the Cameroon and Gabon strains was estimated to have existed around 2008. Comparing our sequences to the vaccine strains, 19 and 15 amino acid (aa) substitutions were observed in the immuno-protective prM-E protein segments of the Dengvaxia® and TetraVax-DV-TV003 vaccines, respectively. Epitope mapping revealed mismatches in aa residues at positions E155 and E161 located in the epitope of the human anti-DENV-1 monoclonal antibody HMAb 1F4. The new DENV strains constitute a conserved genomic pool of viruses endemic to the Central African region that needs prospective monitoring to track local viral evolution. Further work is needed to ascertain the performance of emerging therapeutics in DENV strains from the African region.

Causal effects of place, people, and process on rooftop solar adoption through Bayesian inference

While previous studies have established correlations between rooftop solar adoption and various factors, a comprehensive understanding of the underlying causal mechanisms has been limited by the intricate interrelationships among these variables. To address this gap, we propose a Bayesian causal inference approach that examines the interplay of various factors influencing rooftop solar adoption across multiple cities. By employing post-phenomenology, we uncover latent variables encompassing place, people, and process, shedding light on how they shape public responses to emerging energy technologies. We analyze the causal effects of these factors and highlight the significance of housing and built environment attributes in determining energy expenditure and rooftop solar adoption, emphasizing the need for policies that target energy equity. Additionally, we reveal the influence of neighborhood spillovers on adoption, indicating the role of social norms and information diffusion. The observed city-level variability underscores the importance of local contexts and location-specific factors in the adoption process. Furthermore, we highlight the need to consider causal relationships and the indirect effects of people-related attributes mediated through place-related attributes. Overall, these findings contribute to a deeper understanding of the factors shaping rooftop solar adoption via causal modeling and underscore the importance of tailored policies to promote adoption.

Demonstrating software reliability using possibly correlated tests: Insights from a conservative Bayesian approach

AbstractThis paper presents Bayesian techniques for conservative claims about software reliability, particularly when evidence suggests the software's executions are not statistically independent. We formalise informal notions of “doubting” that the executions are independent, and incorporate such doubts into reliability assessments. We develop techniques that reveal the extent to which independence assumptions can undermine conservatism in assessments, and identify conditions under which this impact is not significant. These techniques – novel extensions of conservative Bayesian inference (CBI) approaches – give conservative confidence bounds on the software's failure probability per execution. With illustrations in two application areas – nuclear power‐plant safety and autonomous vehicle (AV) safety – our analyses reveals: (1) the confidence an assessor should possess before subjecting a system to operational testing. Otherwise, such testing is futile – favourable operational testing evidence will eventually decrease one's confidence in the system being sufficiently reliable; (2) the independence assumption supports conservative claims sometimes; (3) in some scenarios, observing a system operate without failure gives less confidence in the system than if some failures had been observed; (4) building confidence in a system is very sensitive to failures – each additional failure means significantly more operational testing is required, in order to support a reliability claim.

Improving estimates of waning immunity rates in stochastic SIRS models with a hierarchical framework

As most disease causing pathogens require transmission from an infectious individual to a susceptible individual, continued persistence of the pathogen within the population requires the replenishment of susceptibles through births, immigration, or waning immunity.Consider the introduction of an unknown infectious disease into a fully susceptible population where it is not known how long immunity is conferred once an individual recovers from infection. If, initially, the prevalence of disease increases (that is, the infection takes off), the number of infectives will usually decrease to a low level after the first major outbreak. During this post-outbreak period, the disease dynamics may be influenced by stochastic effects and there is a non-zero probability that the epidemic will die out. Die out in this period following the first major outbreak is known as an epidemic fade-out. If the disease does not die out, the susceptible population may be replenished by the waning of immunity, and a second wave may start.In this study, we investigate if the rate of waning immunity (and other epidemiological parameters) can be reliably estimated from multiple outbreak data, in which some outbreaks display epidemic fade-out and others do not. We generated synthetic outbreak data from independent simulations of stochastic SIRS models in multiple communities. Some outbreaks faded-out and some did not. We conducted Bayesian parameter estimation under two alternative approaches: independently on each outbreak and under a hierarchical framework. When conducting independent estimation, the waning immunity rate was poorly estimated and biased towards zero when an epidemic fade-out was observed. However, under a hierarchical approach, we obtained more accurate and precise posterior estimates for the rate of waning immunity and other epidemiological parameters. The greatest improvement in estimates was obtained for those communities in which epidemic fade-out was observed.Our findings demonstrate the feasibility and value of adopting a Bayesian hierarchical approach for parameter inference for stochastic epidemic models.

Physics-informed Bayesian inference of external potentials in classical density-functional theory.

The swift progression and expansion of machine learning (ML) have not gone unnoticed within the realm of statistical mechanics. In particular, ML techniques have attracted attention by the classical density-functional theory (DFT) community, as they enable automatic discovery of free-energy functionals to determine the equilibrium-density profile of a many-particle system. Within classical DFT, the external potential accounts for the interaction of the many-particle system with an external field, thus, affecting the density distribution. In this context, we introduce a statistical-learning framework to infer the external potential exerted on a classical many-particle system. We combine a Bayesian inference approach with the classical DFT apparatus to reconstruct the external potential, yielding a probabilistic description of the external-potential functional form with inherent uncertainty quantification. Our framework is exemplified with a grand-canonical one-dimensional classical particle ensemble with excluded volume interactions in a confined geometry. The required training dataset is generated using a Monte Carlo (MC) simulation where the external potential is applied to the grand-canonical ensemble. The resulting particle coordinates from the MC simulation are fed into the learning framework to uncover the external potential. This eventually allows us to characterize the equilibrium density profile of the system by using the tools of DFT. Our approach benchmarks the inferred density against the exact one calculated through the DFT formulation with the true external potential. The proposed Bayesian procedure accurately infers the external potential and the density profile. We also highlight the external-potential uncertainty quantification conditioned on the amount of available simulated data. The seemingly simple case study introduced in this work might serve as a prototype for studying a wide variety of applications, including adsorption, wetting, and capillarity, to name a few.

Phylogeny and morphological evolution of the Neotropical genus Cuspidaria (Bignonieae, Bignoniaceae): Combining high‐throughput sequencing and targeted loci data

AbstractThe combined use of high‐throughput sequencing (HTS) with targeted loci data offers an excellent opportunity to reconstruct robust and comprehensive phylogenies at fine taxonomic scales using a hybrid approach. In this study, we infer the phylogeny of Cuspidaria (Bignonieae, Bignoniaceae), a diverse clade of lianas and shrubs centered in South America's wet and dry forests. We used HTS to obtain complete or nearly complete plastome sequences of seven individuals of Cuspidaria selected to represent the main clades in the genus. This sampling strategy aimed to reconstruct relationships at deeper nodes (i.e., backbone). We also used targeted loci data obtained through Sanger sequencing to obtain sequences of two chloroplast markers (ndhF, rpl32‐trnL) and one nuclear marker (PepC) for multiple individuals of 18 out of the 21 species of Cuspidaria recognized in the most recent treatment of the genus; only C. emmonsii, C. lachnaea, and C. simplicifolia were not sampled. This broad sampling strategy aimed to test the monophyly of individual species and reconstruct fine‐scale interspecific relationships within the genus. Both datasets were analyzed separately and combined using Bayesian inference and maximum likelihood approaches. The combined dataset includes 65 individuals representing 18 previously recognized species of Cuspidaria plus outgroup taxa. The analysis of the combined dataset recovered a monophyletic Cuspidaria, excluding C. bracteata, which was nested within the outgroup and is best treated elsewhere. Ancestral character state reconstructions provided novel insights into the evolution of morphology and identified multiple putative morphological synapomorphies for key lineages of Cuspidaria. Namely, anthers curved forward was reconstructed as a potential synapomorphy for the Cuspidaria clade, whereas interpetiolar gland fields in stems and inflorescence, subulate or inconspicuous prophylls of the axillary buds, biternate leaves, actinodromous venation, calyx cupulate or spathaceous, calyx apices dentate or irregularly lobed, fruits with winged valves, and fruit midrib limited by two longitudinal ridges were reconstructed as potential synapomorphies of different clades within the genus. Other traits such as habit, tertiary venation, inflorescence type, corolla color, number of ovule series, and pollen unit were highly labile. As circumscribed here, Cuspidaria is composed of eight main clades supported by molecular data and morphological synapomorphies: Sideropogon, Tetrastichella, Cinerea, Paracarpaea, Cremastus, Blepharitheca, Saldanhaea, and Cuspidaria s.str. clades. All species are monophyletic, except for C. pulchra and C. sceptrum, which form a species complex and are best treated as a single taxon. The necessary taxonomic changes are proposed, leading to the recognition of 19 species in Cuspidaria.

Integrative taxonomic revision of the Metarctia Walker, 1855 subgenus HebenaWalker, 1856, with descriptions of six new species and one new subspecies(Lepidoptera: Erebidae: Arctiinae: Syntomini)

A revision of the subgenus Hebena Walker, 1856 of the syntomine genus Metarctia Walker, 1855 is given based on integrative taxonomic analyses. Six new species (Metarctia (Hebena) manfredi sp. n., M. (H.) elleni sp. n., M. (H.) brigitta sp. n., M. (H). lukaszi sp. n., M. (H.) smithi sp. n., and M. (H.) haraldsulaki sp.n.) and one new subspecies (M. (H.) smithi transvallesiana ssp. n.) are described. Metarctia (H.) kelleni (Snellen, 1886) stat. rev. is reinstated from synonymy with M. (H.) rubra (Walker, 1856) and Metarctia (H.) subincarnata Kiriakoff, 1954 syn. n. is synonymised with M. (H.) henrardi Kiriakoff, 1953 and Metarctia cinnamomea (Wallengren, 1860) syn. n. with M. (H.) rubra (Walker, 1856). DNA barcodes were obtained for 116 specimens representing 7 taxa, and genetic analyses were performed using Maximum Likelihood and Bayesian Inference approaches; a DNA barcode tree resulting from the latter is illustrated. Pairwise distances of barcodes between taxa are provided where available. The adults and genitalia of all taxa, their habitats and distribution are illustrated in 19 colour plates and 5 distribution maps.

A Bayesian machine learning method to explain the error characteristics of global-scale soil moisture products

Estimating accurate surface soil moisture (SM) dynamics from space, and knowing the error characteristics of these estimates, is of great importance for the application of satellite-based SM data throughout many Earth Science/Environmental Engineering disciplines. Here, we introduce the Bayesian inference approach to analyze the error characteristics of widely used passive and active microwave satellite-derived SM data sets, at different overpass times, acquired from the Soil Moisture Active Passive (SMAP), Soil Moisture and Ocean Salinity (SMOS), and Advanced Scatterometer (ASCAT) missions. In particular, we apply Bayesian hierarchical modeling (BHM) and triple collocation analysis (TCA) to investigate the relative importance of different environmental factors and human activities on the accuracy of satellite-based data.To start, we compare the BHM-based sensitivity analysis method to the classic multiple regression models using a frequentist approach, which includes complete pooling and no-pooling models that have been widely used for sensitivity analysis in the field of remote sensing and demonstrate the BHM's adaptability and great potential for providing insight into sensitivity analysis that can be used by various remote sensing research communities.Next, we conduct an uncertainty analysis on BHM's model parameters using a full range of uncertainties to assess the association of various environmental factors with the accuracy of satellite-derived SM data. We focus on investigating human-induced error sources such as disturbed surface soil layers caused by irrigation activities on microwave satellite systems, naturally introduced error sources such as vegetation and soil organic matter, and errors related to the disregard of SM retrieval algorithmic assumptions - such as the thermal equilibrium passive microwave systems. Based on the BHM-based sensitivity analysis, we find that assessments of SM data quality with a single variable should be avoided, since numerous other factors simultaneously influence their quality. As such, this provides a useful framework for applying Bayesian theory to the investigation of the error characteristics of satellite-based SM data and other time-varying geophysical variables.