Distribution Model Research Articles

Predicting the distribution of Ixodes ricinus in Europe: integrating microclimatic factors into ecological niche models.

Ixodes ricinus, commonly known as the castor bean tick and sheep tick, is a significant vector of various diseases, such as tick-borne encephalitis and Lyme borreliosis. Owing to climate change, the distribution and activity of I. ricinus are expected to increase, leading to an increase in the number of diseases transmitted by this species. Most distribution models and ecological niche models utilize macroclimate datasets such as WorldClim or CHELSA to map the distribution of disease-transmitting ticks. However, microclimatic factors are crucial for the activity and survival of small arthropods. In this study, an ecological niche modelling approach was used to assess the climatic suitability of I. ricinus using both microclimatic and macroclimatic parameters. A Mixed model was built by combining parameters from the Soiltemp (microclimate) and Wordclim (macroclimate) databases, whereas a Macroclimate model was built with the CHELSA dataset. Additionally, future suitabilities were projected via the macroclimate model under the SSP3-7.0 and SSP5-8.5 scenarios. Macroclimate and Mixed models showed similar distributions, confirming the current distribution of I. ricinus. The most important climatic factors were seasonality, annual temperature range, humidity and precipitation. Future projections suggest significant expansion in northern and eastern Europe, with notable declines in southern Europe.

Study of the Stress Distribution and a Calculation Model for the Local Bearing Capacity of Concrete Under Headed Bars

In order to obtain the calculation model for the local bearing capacity of concrete Fl under two headed bars, six pull-out concrete specimens were prepared. The effect of the net distance between two headed bars c on Fl was mainly investigated. The test results show that the local bearing capacity of concrete would first decrease and then increase with the increase in c, and the boundary point of the two stages was c = 40 mm. It is determined that the stress transformation from the local compression state to the axial compression state in the stress distribution model is characterized by the variation rate of the vertical stress under individual headed bars, which would infinitely approach a constant value. The constant value under individual headed bars is used as the limit value. The height of the vertical stress under two headed bars is modified, and then the height of the tensile region of the specimen with different c values is determined. Combined with the experimental phenomena and the results, two stages of calculation models are established, respectively: the integral calculation model and the individual calculation model. The integral calculation model focuses on the interaction of the compression region under two headed bars. The individual calculation model mainly focuses on the interaction of the tensile region under two headed bars. The calculation equations considering the influence of the height of the tensile region are established. Two groups of similar test data regarding the local bearing capacity were collected and verified as the integral calculation model and the individual calculation model. The average value of the ratio between the test value and the calculated value is 1.057 and 1.061, the standard deviation is 0.153 and 0.091, and the coefficient of variation is 0.055 and 0.086. It is proved that the calculation model proposed in this paper has a certain accuracy. It can provide a reference for calculating the local bearing capacity of concrete under multiple headed bars.

Using Earth observation to support the predictive accuracy of species distribution models in ecological restoration: a case study of Poland beavers (Castor fiber)

Abstract. The ability of Earth observation and remote sensing to identify the potential of species to restore ecosystems is a critical element in combating habitat degradation and biodiversity loss. Historical climate data and current species occurrence, coupled with geospatial modelling methods, can significantly aid in decision-making to support ecosystem restoration. We chose Eurasian beavers (spp. Castor fiber) as our case species, which are known ecosystem engineers that create wetland habitats and significantly alter/reinforce the morphology, dynamics, and hydraulics of riverine landscapes. To investigate their ecosystem restoration potential, we applied species distribution modelling (SDM) to determine their current and future distribution scenarios. Our SDMs used current and simulated future environmental data along with different classes of rasters such as elevation, inland swamps, water bodies, natural seasonally or permanently wet grasslands, open mires, riparian mixed forests, riparian swamp broadleaved forests, riparian swamp coniferous forests, watercourses, protected areas, and urban and built-up areas derived from Google Earth Engine to predict areas suitable for beaver habitats. Further, by using the zonation method, we were able to develop a stepping stone model to prioritize habitat conservation efforts. We provide habitat projections according to time periods of "2041–2060", "2061–2080" and "2081–2100", and to different climate change scenarios (SSP126, SSP245, SSP370, and SSP585). Thus, using a potential nature-based solution, we have employed remote sensing data and tools to develop a holistic framework for not only quantifying the ecosystem restoration potential of beavers but will also provide a new direction for understanding species-specific importance in climate change mitigation.

Dust mass in protoplanetary disks with porous dust opacities

Atacama Large Millimeter/submillimeter Array surveys have suggested that protoplanetary disks are not massive enough to form the known exoplanet population, based on the assumption that the millimeter continuum emission is optically thin. In this work, we investigate how the mass determination is influenced when the porosity of dust grains is considered in radiative transfer models. The results show that disks with porous dust opacities yield similar dust temperatures, but systematically lower millimeter fluxes, as compared to disks that incorporate compact dust grains. Moreover, we have recalibrated the relation between dust temperature and stellar luminosity for a wide range of stellar parameters. We also calculated the dust masses of a large sample of disks using the traditionally analytic approach. The median dust mass from our calculation is about six times higher than the literature result, and this is mostly driven by the different opacities of porous and compact grains. A comparison of the cumulative distribution function between disk dust masses and exoplanet masses shows that the median exoplanet mass is about two times lower than the median dust mass when grains are assumed to be porous and there are no exoplanetary systems with masses higher than the most massive disks. Our analysis suggests that adopting porous dust opacities may alleviate the mass budget problem for planet formation. As an example illustrating the combined effects of optical depth and porous dust opacities on the mass estimation, we conducted new IRAM/NIKA-2 observations toward the IRAS\,04370+2559 disk and performed a detailed radiative transfer modeling of the spectral energy distribution (SED). The best-fit dust mass is roughly 100 times higher than the value given by a traditionally analytic calculation. Future spatially resolved observations at various wavelengths are required to better constrain the dust mass.

Confidence interval estimation for reliability of a two parameter Weibull distribution under complete and censored samples

Estimating the reliability function of the two parameter Weibull distribution is a critical task in various fields, providing essential insights into product longevity and optimizing maintenance strategies. Due to its adaptable parameters namely scale parameter α and shape parameter β, the Weibull distribution models diverse failure behaviours, from early-life defects to wear-out periods. By evaluating reliability at key time phases namely early life (t < 0.1α), useful life (0.1α ≤ t ≤ α), and end-of-life (t > α), this estimation process supports quality control, operational forecasting, and end-of-life planning. Such reliability assessments help minimize operational disruptions, improve cost management, and support strategic planning across industries where dependability and lifecycle management are essential, such as in manufacturing, healthcare, and technology sectors. Although it holds practical significance, the estimation of confidence interval (CI) for the reliability function of two parameter Weibull distribution has been relatively underexplored in the literature. This paper presents a new approach for constructing CI for the reliability function of the Weibull distribution using the generalized variable (GV) technique, applicable to both complete samples and type II singly right-censored samples. The empirical evaluation of this method indicates that it provides coverage probabilities that are closely aligned with the nominal level, even when dealing with small uncensored samples (as small as 5) and censored samples where the proportion of censored observations can reach 70%. In comparison, traditional methods for the Weibull distribution tend to yield less reliable or widely varied coverage probabilities for complete samples. The findings are demonstrated through practical examples. Keywords: Reliability, Confidence interval, Generalized Variable approach, Censoring.

Distributed model predictive control for asynchronous multiagent systems with self‐triggered coordinator

AbstractThis paper investigates a distributed model predictive control strategy for asynchronous nonlinear multiagent systems (MASs) with external disturbances via self‐triggered generators and prediction horizon regulators. First, a shrinking constraint related to the error between the actual state and the predicted state is introduced into the optimal control problem to enhance the robustness of the system. Then, the triggering interval and the corresponding prediction horizon are determined by altering the expression of the Lyapunov function, thus achieving a trade‐off between control performance and energy loss. By implementing the proposed algorithm, the coordination objective of the MASs is achieved under asynchronous communication. Finally, the recursive feasibility and closed‐loop stability are proven successively. An illustrative example is conducted to demonstrate the merits of the presented approach.

Comparative analysis of climate-induced habitat shift of economically significant species with diverse ecological preferences in the Northwest Pacific

The Northwest Pacific Ocean is the most productive fishing ground in the Pacific Ocean, with a continuous rise in water temperature since 1990. We developed stacked species distribution models (SSDMs) to estimate the impacts of climate change on the distribution dynamics of economically significant species under three climate change scenarios for the periods 2040-2060 and 2080-2100. Overall, water temperature is the most important factor in shaping the distribution patterns of species, followed by water depth. The predictive results indicate that all the species show a northward migration in the future, and the migration distance varies greatly among species. Most pelagic species will expand their habitats under climate change, implying their stronger adaptability than benthic species. Tropical fishes are more adaptable to climate change than species in other climate zones. Though limitations existed, our study provided baseline information for designing a climate-adaptive, dynamic fishery management strategy for maintaining sustainable fisheries.

Uncertainty quantification for seismic response using dimensionality reduction‐based stochastic simulator

AbstractThis paper introduces a stochastic simulator for seismic uncertainty quantification, which is crucial for performance‐based earthquake engineering. The proposed simulator extends the recently developed dimensionality reduction‐based surrogate modeling method (DR‐SM) to address high‐dimensional ground motion uncertainties and the high computational demands associated with nonlinear response history analyses. By integrating physics‐based dimensionality reduction with multivariate conditional distribution models, the proposed simulator efficiently propagates seismic input into multivariate response quantities of interest. The simulator can incorporate both aleatory and epistemic uncertainties and does not assume distribution models for the seismic responses. The method is demonstrated through three finite element building models subjected to synthetic and recorded ground motions. The proposed method effectively predicts multivariate seismic responses and quantifies uncertainties, including correlations among responses.

Resilience of cushion Astragalus to climate change in the Zagros Biodiversity hotspot: a species distribution modeling approach

Resilience of cushion Astragalus to climate change in the Zagros Biodiversity hotspot: a species distribution modeling approach

Light Distribution Models for Tree Growth Simulation

AbstractThe simulation and modelling of tree growth is a complex subject with a long history and an important area of research in both computer graphics and botany. For more than 50 years, new approaches to this topic have been presented frequently, including several aspects to increase realism. To further improve these achievements, we present a compact and robust functional‐structural plant model (FSPM) that is consistent with botanical rules. While we show several extensions to typical approaches, we focus mainly on the distribution of light as a resource in three‐dimensional space. We therefore present four different light distribution models based on ray tracing, space colonization, voxel‐based approaches and bounding volumes. By simulating individual light sources, we were able to create a more specified scene setup for plant simulation than it has been presented in the past. By taking into account such a more accurate distribution of light in the environment, this technique is capable of modelling realistic and diverse tree models.

Sensitivity Analysis of Simulation-Based Inference for Galaxy Clustering

Abstract Simulation-based inference (SBI) is a promising approach to leverage high fidelity cosmological simulations and extract information from the non-Gaussian, non-linear scales that cannot be modeled analytically. However, scaling SBI to the next generation of cosmological surveys faces the computational challenge of requiring a large number of accurate simulations over a wide range of cosmologies, while simultaneously encompassing large cosmological volumes at high resolution. This challenge can potentially be mitigated by balancing the accuracy and computational cost for different components of the the forward model while ensuring robust inference. To guide our steps in this, we perform a sensitivity analysis of SBI for galaxy clustering on various components of the cosmological simulations: gravity model, halo-finder and the galaxy-halo distribution models (halo-occupation distribution, HOD). We infer the σ8 and Ωm using galaxy power spectrum multipoles and the bispectrum monopole assuming a galaxy number density expected from the luminous red galaxies observed using the Dark Energy Spectroscopy Instrument (DESI). We find that SBI is insensitive to changing gravity model between N-body simulations and particle mesh (PM) simulations. However, changing the halo-finder from friends-of-friends (FoF) to Rockstar can lead to biased estimate of σ8 based on the bispectrum. For galaxy models, training SBI on more complex HOD leads to consistent inference for less complex HOD models, but SBI trained on simpler HOD models fails when applied to analyze data from a more complex HOD model. Based on our results, we discuss the outlook on cosmological simulations with a focus on applying SBI approaches to future galaxy surveys.

Predicting Electric Vehicle Charging Demand in Residential Areas Using POI Data and Decision‐Making Model

Transport electrification is a crucial element of the ongoing energy transition, essential for achieving carbon peaking and carbon neutrality goals. The proliferation of electric vehicles (EVs) introduces significant challenges to power distribution network stability due to their aggregated charging load in residential areas, particularly during peak electricity consumption periods. This paper proposes a method to predict the spatiotemporal distribution of EV charging demand in residential areas using geographic information points of interest (POI) data features and a decision‐making model. Utilizing real historical data, probability distribution models for EV users' arrival times and charging characteristics were constructed using Gaussian Mixture Models (GMM). The spatiotemporal characteristics of EV travel and charging behaviors were analyzed, and a comprehensive charging decision model incorporating both emergency and stochastic scenarios was developed. The model's efficacy in capturing the probability distributions of characteristic variables was validated through a case study. The results demonstrate the model's potential for accurately predicting EV charging demand, providing valuable insights for infrastructure planning and resource allocation. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Quercus purhepecha (Fagaceae), a new species of shrub oak endemic to the state of Michoacán, Mexico

Within the genus Quercus there are species with a shrubby habit that have been little studied worldwide. In Mexico, several shrub species within section Quercus are taxonomically highly problematic. These include eight named taxa that share traits such as abaxially tomentose leaves and growth of stems from one or multiple rhizomes, and there is evidence of additional undescribed species. Here, Quercus purhepecha is described and illustrated as a new tomentose shrubby white oak species, with a very restricted distribution within the Cuitzeo basin in the Trans-Mexican Volcanic Belt (TMVB), Michoacán state, Mexico. This new species is morphologically compared with Q. frutex, Q. microphylla and Q. repanda, which are similar taxa also distributed in the TMVB. In addition, a potential distribution model of Q. purhepecha for the Cuitzeo basin is presented.

Variational inference for acceleration of SN Ia photometric distance estimation with BayeSN

Abstract Type Ia supernovae (SNe Ia) are standarizable candles whose observed light curves can be used to infer their distances, which can in turn be used in cosmological analyses. As the quantity of observed SNe Ia grows with current and upcoming surveys, increasingly scalable analyses are necessary to take full advantage of these new datasets for precise estimation of cosmological parameters. Bayesian inference methods enable fitting SN Ia light curves with robust uncertainty quantification, but traditional posterior sampling using Markov Chain Monte Carlo (MCMC) is computationally expensive. We present an implementation of variational inference (VI) to accelerate the fitting of SN Ia light curves using the BayeSN hierarchical Bayesian model for time-varying SN Ia spectral energy distributions (SEDs). We demonstrate and evaluate its performance on both simulated light curves and data from the Foundation Supernova Survey with two different forms of surrogate posterior – a multivariate normal and a custom multivariate zero-lower-truncated normal distribution – and compare them with the Laplace Approximation and full MCMC analysis. To validate of our variational approximation, we calculate the pareto-smoothed importance sampling (PSIS) diagnostic, and perform variational simulation-based calibration (VSBC). The VI approximation achieves similar results to MCMC but with an order-of-magnitude speedup for the inference of the photometric distance moduli. Overall, we show that VI is a promising method for scalable parameter inference that enables analysis of larger datasets for precision cosmology.

The VST ATLAS Quasar Survey III: Halo mass function via quasar clustering and quasar-CMB lensing cross-clustering

Abstract We exploit the VST ATLAS quasar/QSO catalogue to perform three measurements of the quasar halo mass profile. First, we make a new estimate of the angular auto-correlation function of ≈230, 000 ATLAS quasars with zphoto ≲ 2.5 and 17 < g < 22. By comparing with the ΛCDM mass clustering correlation function, we measure the quasar bias to be bQ ≈ 2.1, implying a quasar halo mass of Mhalo ≈ 8.5 × 1011h−1M⊙. Second, we cross-correlate these z ≈ 1.7 ATLAS quasars with the Planck Cosmic Microwave Background (CMB) lensing maps, detecting a somewhat stronger signal at 4′ < θ < 60′ than previous authors. Scaling these authors’ model fit to our data we estimate a quasar host halo mass of Mhalo ≈ 8.3 × 1011h−1M⊙. Third, we fit Halo Occupation Distribution (HOD) model parameters to our quasar auto-correlation function and from the derived halo mass function we estimate a quasar halo mass of Mhalo ≈ 2.5 × 1012h−1M⊙. We then compare our HOD model prediction to our quasar-CMB lensing result, confirming their consistency. We find that most (≈2/3) QSOs have halo masses within a factor of ≈3 of this average mass. An analysis based on the probability of X-ray detections of AGN in galaxies and the galaxy stellar mass function gives a similarly small mass range. Finally, we compare the quasar halo mass and luminosity functions and suggest that gravitational growth may produce the constant space density with redshift seen in the quasar luminosity function.

Palliative Care in the Midst of Adversity: Insights from a Low-Income Country.

The integration of palliative care into conventional healthcare services has demonstrated significant benefits in alleviating serious health-related suffering (SHRS), reducing symptom burden, and lowering healthcare expenditure for patients and families. Despite Lebanon's initial steps towards palliative care development, its integration remains inadequate, particularly due to the country's socio-political and economic challenges. This paper examines the current landscape of palliative care services in Lebanon, including their geographical distribution and care models, while addressing the obstacles impeding their progress. Data on palliative care services were gathered from the presentations of stakeholders at the 2023 National Palliative Care Conference. Currently, three nonprofit organizations provide home-based palliative care primarily for end-of-life patients, while seven hospitals offer palliative care through inpatient consultation teams, three of which have specialized palliative care wards and two outpatient clinics. Hospital-based palliative care services are primarily located in the capital city Beirut and operate within private healthcare facilities. Notably, only home-based services are provided free of charge, while hospital-based care is not reimbursed, thus limiting access to palliative care for only those who can afford it. The political and economic instability, inadequate policies and insufficient reimbursement, shortage of trained expertise and essential medicines like morphine, and inconsistent health education are some of the challenges that Lebanon faces in developing palliative care. A multilevel coordinated response and advocacy are crucial to drive policy reforms, enhance education, promote public awareness, and improve clinical practice ensuring quality and equitable palliative care access to all.

The VST ATLAS Quasar Survey II: Halo mass profiles of galaxies, LRGs and galaxy clusters via quasar and CMB lensing

Abstract We cross-correlate a low-contamination subset of the VST ATLAS g < 22.5 quasar catalogue with g < 21.5 galaxy clusters, r < 21 galaxies and r < 19.5 Luminous Red Galaxies (LRGs) to probe their halo mass profiles via quasar magnification bias caused by weak lensing. In the case of galaxy clusters we find that at small scales their mass profiles are well fitted by Navarro, Frenk and White (NFW) models with masses within the expected range. For the galaxies, we find consistency with previous SDSS-based results for the galaxy-quasar cross-correlation and the galaxy auto-correlation functions. Disagreement as to whether the cross-correlation results are in tension with ΛCDM appears due to different assumptions as to whether galaxies trace mass. We conclude that halo occupation distribution (HOD) models fit the galaxy - quasar lensing results better than models where galaxies trace the mass. We further test the cluster and galaxy HOD models in the 2-halo range using the Planck Cosmic Microwave Background (CMB) lensing map, finding that the cross-correlation with both the poorest clusters and the galaxies may be marginally over-predicted by the above HOD models. Finally, we measure the magnification bias of LRGs using both quasar and CMB lensing and find that the observed quasar lensing amplitude may be ≈2 × too high and, on larger scales, the CMB lensing amplitude may be too low to be explained by a standard LRG HOD model.

Unravelling the distribution of mycorrhizal plants in the Colombian páramos

AbstractThe tropicalpine páramo stands out as a unique biodiversity hotspot located in the northern Andes. Over the last decades, páramo plants and animals have been increasingly studied, yet a critical knowledge gap remains on their microbial ecology. Given the crucial importance of mycorrhiza in structural and functional communities, our study aimed to explore their geographic distribution in the Colombian páramos. Specifically, we used information about their host plants as a proxy and modelled their current distribution to unravel spatial patterns related to mycorrhizal status and mycorrhizal types. We constructed species distribution models for 440 vascular plant species representatives of the Colombian páramos using a combination of climatic and edaphic variables. Then we stacked the models following species classification into their mycorrhizal type: arbuscular, ectomycorrhizal, ericoid, orchid, and non-mycorrhizal. Our results suggest that the abundance of mycorrhizal species decreases with increasing elevation and in the west-to-east direction. The arbuscular type of mycorrhiza is chiefly dominant and spreads out widely throughout the Colombian páramos. The other types showed singular patterns, with (i) species having ericoid mycorrhiza being abundant in both the southern (Nariño–Putumayo) and the northern páramos (Sierra Nevada–Perijá); (ii) species with orchid mycorrhiza being abundant in the Western Cordillera and overall at low elevations, (iii) ectomycorrhizal species being absent, and (iv) non-mycorrhizal species being widely spread and reaching high elevations. Our study highlights the potentially high level of mycorrhization of this alpine system and contributes new spatial and environmental information towards a better understanding of mycorrhizal biogeography in tropical mountains.

Mixed mode stress intensity factor analysis on edge cracked FGM plate with different material distribution models by XFEM

Mixed mode stress intensity factor analysis on edge cracked FGM plate with different material distribution models by XFEM

The role of remote sensing in species distribution models: a review

The role of remote sensing in species distribution models: a review