Generalized Distribution Model Research Articles

Modelling heterogeneity in the classification process in multi-species distribution models can improve predictive performance.

Species distribution models and maps from large-scale biodiversity data are necessary for conservation management. One current issue is that biodiversity data are prone to taxonomic misclassifications. Methods to account for these misclassifications in multi-species distribution models have assumed that the classification probabilities are constant throughout the study. In reality, classification probabilities are likely to vary with several covariates. Failure to account for such heterogeneity can lead to biased prediction of species distributions. Here, we present a general multi-species distribution model that accounts for heterogeneity in the classification process. The proposed model assumes a multinomial generalised linear model for the classification confusion matrix. We compare the performance of the heterogeneous classification model to that of the homogeneous classification model by assessing how well they estimate the parameters in the model and their predictive performance on hold-out samples. We applied the model to gull data from Norway, Denmark and Finland, obtained from the Global Biodiversity Information Facility. Our simulation study showed that accounting for heterogeneity in the classification process increased the precision of true species' identity predictions by 30% and accuracy and recall by 6%. Since all the models in this study accounted for misclassification of some sort, there was no significant effect of accounting for heterogeneity in the classification process on the inference about the ecological process. Applying the model framework to the gull dataset did not improve the predictive performance between the homogeneous and heterogeneous models (with parametric distributions) due to the smaller misclassified sample sizes. However, when machine learning predictive scores were used as weights to inform the species distribution models about the classification process, the precision increased by 70%. We recommend multiple multinomial regression to be used to model the variation in the classification process when the data contains relatively larger misclassified samples. Machine learning prediction scores should be used when the data contains relatively smaller misclassified samples.

A dynamic model of a vertical direct expansion ground heat exchanger

The paper presents a general distributed model of a vertical U-tube direct expansion heat exchanger coupled with the ground. This model is developed for studying the dynamic thermal behavior of a buried heat exchanger (the evaporator or condenser) which is an integral part of the so-called direct expansion heat pump. The transient conservative equations of mass and energy considering the two-phase flow of refrigerant are derived and presented. The heat exchange with the ground is treated using an analytical approach to treat short time response of vertical boreholes based on an imposed heat flux. The thermal interference between the two pipes of the heat exchanger is also considered. The mathematical equations of the model are numerically presented using a finite difference scheme and the solution of the system of equations is obtained using a built-in iterative solver function of MatLab®. The dynamic behavior of a condenser and an evaporator are first obtained by imposing a change of the inlet conditions. Experimental data from a test rig are used to demonstrate the validity of the dynamic model

Catchment‐scale applications of hydraulic habitat models: Climate change effects on fish

AbstractApproaches available for estimating the ecological impacts of climate change on aquatic communities in river networks range from detailed mechanistic models applicable locally to correlative approaches applicable globally. Among them, hydraulic habitat models (HABMs) link hydraulic models of streams with biological models that reflect how organisms select microhabitat hydraulics. Coarser but more general species distribution models (SDMs) predict changes in geographic distributions; they generally involve coarse predictors such as air temperature or distance to source but neglect proximate habitat descriptors such as microhabitat hydraulics. We propose an original application of HABM for predicting the ecological impacts of climate change at large scales, a comparison of their predictions with those of SDM and a linkage of the two modelling approaches. We showcase our approach in a large catchment (Rhône River) where an available distributed hydrological model estimates present and future unregulated daily flows over the whole river network. Despite large local uncertainties, simulations showed that climate change may strongly reduce low flow percentiles (e.g., a median reduction of 38.6% for a pessimistic climate scenario), inducing important alteration of fish hydraulic habitat suitability (e.g., a median loss of 3.9%–18.7% for three modelled fish species with contrasting habitat use: brown trout, barbel and sculpin). The HABM and SDM individually predicted consistent or opposite fish responses to climate change, depending on the species and their habitat requirements. Our results illustrate that accounting for ecological responses to proximate habitat variables such as hydraulics can strongly modify projections related to climate change.

Ecological niche models reveal divergent habitat use of Pallas's cat in the Eurasian cold steppes.

Identifying the association between the patterns of niche occupation and phylogenetic relationships among sister clades and assisting conservation planning implications are of the most important applications of species distribution models (SDMs). However, most studies have been carried out regardless of within taxon genetic differentiation and the potential of local adaptation occurring within the species level. The Pallas's cat (Otocolobus manul) is a less-studied species with unknown biogeography and phylogenetic structure across a widespread yet isolated range from the Caucasus to eastern China. In the current study, by considering a previously proposed genetic structure and based on a cluster analysis on climatic variables, we supposed three clades for this species, including O. m. manul, O. m. ferrugineus, and O. m. nigripectus. We developed SDM for each clade separately and compared it with a general distribution model of the species to determine whether the hypothesized taxonomic resolution affects the predicted ecological niche of the within-species structures. We assessed the effect of climate change on the future distribution of the species to detect the most sensitive clades to global warming scenarios. Our results showed that for all clades' models, the AUC and TSS were greater than the general model. Access to the preferred prey of the Pallas's cat, that is, pika, had a significant effect on the distribution of O. m. manul and O. m. ferrugineus, whereas the most influential variable affecting O. m. nigripectus habitat suitability was terrain slope. Based on our future projections, we found that future climate change likely threatens the clades O. m. ferrugineus and O. m. nigripectus more than O. m. manul, findings that were hidden in the general model. Our results highlight the proficiency of SDMs in recognizing within-taxon habitat use of widespread species and the necessity of this procedure for implementing effective conservation planning of these species.

Realistic and general model for quantum key distribution with entangled-photon sources

Entanglement-based quantum key distribution (EB-QKD) has several advantages over prepare-and-measure protocols, such as eliminating the need for decoy states and active modulators. At present, entanglement sources based on the spontaneous parametric down-conversion process have been widely used in EB-QKD experiments. Most of the existing models regard the photon-number distribution of pulse-pumped entanglement sources as thermal. However, doing so ignores any effects of spectral modes, which will lead to the overestimation of the secure key rate. In this paper, we analyze the correlation between the photon-number distribution and the joint spectral distribution and rewrite the quantum state of the photon pairs. Then we apply the results to EB-QKD and present a general model which can be used to calculate the secure key rate not only for any pulse-pumped systems but even for cw-pumped systems.

An Electromagnetic-Flux-Distribution Model for Analyses of Superconducting Josephson Junction Circuits and Quantum Phase-Slip Junction Circuits

The Josephson junctions have brought the developments of superconducting quantum interference devices (SQUIDs) and the single-flux quantum (SFQ) digital integrated circuits. Meanwhile, the quantum phase-slip (QPS) junction is founded to be the dual element of the Josephson junction and is promising for the new devices dual to the Josephson junction circuits. This article presents a general electromagnetic-flux distribution model with the conventional electric variables to unify the analysis methods for the superconducting circuits based on the Josephson junctions and QPS junctions. This model depicts the working principles of the Josephson junction network and its dual circuit with the uniform circuit equations; it also provides the general solution of building the dual circuit of a Josephson junction network with QPS Junctions. The analyses of a Josephson junction network and its dual circuit are demonstrated, and the simulation results based on the electromagnetic-flux distribution model are presented. The general electromagnetic-flux distribution model bridges the gap between the superconducting and non-superconducting circuits.

Optimal resource allocation for flexible-grid entanglement distribution networks.

We use a genetic algorithm (GA) as a design aid for determining the optimal provisioning of entangled photon spectrum in flex-grid quantum networks with arbitrary numbers of channels and users. After introducing a general model for entanglement distribution based on frequency-polarization hyperentangled biphotons, we derive upper bounds on fidelity and entangled bit rate for networks comprising one-to-one user connections. Simple conditions based on user detector quality and link efficiencies are found that determine whether entanglement is possible. We successfully apply a GA to find optimal resource allocations in four different representative network scenarios and validate features of our model experimentally in a quantum local area network in deployed fiber. Our results show promise for the rapid design of large-scale entanglement distribution networks.

General probability distribution model for wireless body sensors in the medical monitoring system

States are making every effort and huge investment to keep human health at the highest level with a growing population. For this purpose, networks of multifunctional sensors have been developed. The main reason affecting the wireless sensors on the body is the noise level. The noise is due to the environment, and the receiver distance that characterizes the channel. The noise is random and most of the time requires pre-knowledge of the source for successful representation. Normally, details about the source help in determining the type of random distribution. This research article first introduces and proves the validity of a new probability distribution function to accurately represent the random noise without pre-knowledge of the source, location, and distance related to the wireless sensors. Next, the performance of the model was tested with a real experimental application. The proposed model has better representation than the most applicable distributions such as Weibull, lognormal, Gaussian, Rayleigh, and Nakagami. The results obtained are promising to apply the introduced model for a realistic random noise representation at very low signal levels. The accurate representation of the noise level is directly related to the channel characteristic and provides a more precise representation of the signal level at the receiver which is of great importance in the designs of the receiver units.

Research on the reliable test range of four-point supported piezoelectric dynamometer

PurposeThe purpose of this paper is to improve the reliability of the force measurement system by determining the reliable test range of dynamometer.Design/methodology/approachBased on the principle of leverage and moment balance, a general force distribution model is applicable in where the test point is located either inside or outside the support region of four three-component force links of dynamometer is established. After corroborating the correctness of the model through verification experiments, the boundary conditions that each three-component force link should satisfy are analyzed by considering the characteristic of the dynamometer components comprehensively. Furthermore, the reliable test range of dynamometer is determined, followed by a calibration experiment to verify its rationality.FindingsThe relationships between the reliable test range and the tested force, the bolt pre-tightening force and the bearing capacity of quartz wafers are clarified. Further, the experimental calibration results show that when the test point is within the reliable test range, the three-directional output voltage of dynamometer has excellent linearity and repeatability. The nonlinearity and repeatability in X-, Y- and Z-directions are all less than 1.1%.Originality/valueA general mathematical model of force distribution of four three-component force links is constructed, which provides a theoretical basic for the mechanical analysis of multi-sensors’ dynamometer. Comprehensively considering the performance of dynamometer components, the value of measured force and the pre-tightening force, the simultaneous equations of reliable test range are deduced, which limits the boundary of allowable test position of piezoelectric dynamometer.

Development of a general model for monitoring moisture distribution of four vegetables undergoing microwave-vacuum drying by hyperspectral imaging

A hyperspectral imaging system was employed to develop a general model for monitoring moisture distribution of four types of vegetables undergoing microwave-vacuum drying (MVD). Partial least squares regression and least squares support vector machines were developed based on full and optimal wavelengths, while the multiple linear regression (MLR) was built from optimal spectral data. Genetic algorithm-MLR (GA-MLR) model with a determination coefficient in prediction of 0.974 and a root mean square error in prediction of 4.70% was used for the moisture content visualization of four MVD vegetables. Moisture distribution maps indicated a similar center drying pattern among different vegetables, while the conner effects occurred in celery stalks and spinach leaves. Additionally, evaluations of color changes of the vegetables using the computer vision system indicated a decrease of the browning index in dried carrot slices and celery stalks but an increase in potato slices and spinach leaves.

Distribution, eco-climatic characterisation, and potential growing regions of Annona cherimola Mill. (Annonaceae) in Mexico.

The cherimoya (Annona cherimola Mill.) is a fruit crop with worldwide commercial importance. However, its distribution and potential growing regions of cherimoya are not defined for Mexico. That is why, this research aims to map the natural distribution of cherimoya and different eco-climatic regions where it is grown in Mexico as well as to map the climatic adaptability with the current climate and a prospection with the climate change scenario, all by different models of GIS. The general distribution model of cherimoya in Mexico showed that it had a chance to find cherimoya “in a natural way” in the biogeographic provinces Trans-Mexican Volcanic Belt, Sierra Madre del Sur and Highlands of Chiapas. Three eco-climatic groups were found in the distribution of cherimoya that corresponded to climates C(m)(w), (A)C(e’), and (A)C(e), respectively. Where the group with climate (A)C(e) had the most restricted distribution. The potential growing regions of excellent adaptation of cherimoya were found in the biogeographic provinces of Trans-Mexican Volcanic Belt, Sierra Madre Occidental, Sierra Madre Oriental, Sierra Madre del Sur and Highlands of Chiapas. Finally, based on eco-crop modelling, it is concluded that climate change will not greatly affect areas of excellent adaptation of cherimoya in Mexico.

Three Different Features Based Metric To Assess Image Quality Blindly

Abstract When creating image quality assessment metric (IQA) no confirmation all distortion types are available. Non-specific distortion blind/no-reference (NR) IQA algorithms mostly need prior knowledge about anticipated distortions. This paper introduce a generic and distortion unaware (DU) approach for IQA with No Reference (NR). The approach uses three different measuring features which are initiated from the gist of natural scenes (NS) using Log-derivatives of the parameters; a general Gaussian distribution model, two sharpness functions, and Weibull distribution. All features were analyzed and co mpared together to examine their performance. When calibrating the proposed features performance on LIVE database, experiments show they have good contribution to the state of the art IQA and they outperform the popular full-reference peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) methods. Also they show sharpness features are the best when assess both prediction monotonicity and predict accuracy evaluation among the three features categories. Besides they show asymmetric generalized Gaussian distribution (AGGD) based features have the best correlation with differential mean opinion score.

On the Cooling Flow Problem in the Gaseous Halo of the Milky Way

Abstract Theoretical and observational arguments suggest that there is a large amount of hot (∼106 K) diffuse gas residing in the Milky Way (MW) halo, but its total mass and spatial distribution are still unclear. In this work, we present a general model for the gas density distribution in the Galactic halo, and investigate the gas evolution under radiative cooling with a series of 2D hydrodynamic simulations. We find that the mass inflow rate in the developed cooling flow increases with gas metallicity and the total gas mass in the halo. For a fixed halo gas mass, the spatial gas distribution affects the onset time of the cooling catastrophe, which starts earlier when the gas distribution is more centrally peaked, but does not substantially affect the final mass inflow rate. The gravity from the Galactic bulge and disk affects gas properties in the inner regions, but has little effect on the final inflow rate either. We confirm our results by investigating cooling flows in several density models adopted from the literature, including the Navarro–Frenk–White (NFW) model, the cored-NFW model, the Maller & Bullock model, and the β model. Typical mass inflow rates in our simulations range from ∼5 M ⊙ yr−1 to ∼60 M ⊙ yr−1, and are much higher than the observed star formation rate in our Galaxy, suggesting that stellar and active galactic nucleus feedback processes may play important roles in the evolution of the MW and MW-type galaxies.

Two-way quantum key distribution in a uniformly distributed quantum space via a special mapping and its analytical security proofs

Within the field of quantum cryptography, two-way direct quantum secure communication is a relatively new proposal for sharing secret information that is not fully explored yet. We propose a general model for two-way deterministic quantum key distribution, and a special mapping from integers to a uniformly distributed quantum space is introduced for qubit pad preparation. A reduced model, more practical, is also proposed by relaxing the security assumption of the qubit pad preparation. The main work of this paper focuses on the security proofs of these two models. Under collective attacks, we fully analyze the security basis of the general model, and, in a relatively simple way, we provide an analytical security proof for the reduced model in a depolarization quantum channel. Our results show exactly the analytical upper-bound of the amount of useful key that a powerful eavesdropper could extract in both models, and the security of the reduced model is not reduced. One advantage of this work is that the special mapping guarantees the random distribution property of the qubit pad in a uniformly distributed quantum space, thus guaranteeing the security of the two models, and the other advantage is the simplicity of the analytical derivations of security proofs.

Development of powder distribution model of discontinuous coaxial powder stream in laser direct metal deposition

For laser direct metal deposition (LDMD) process, a general powder distribution model of discontinuous coaxial powder stream (DCPS) was developed based on homogeneous transformation. An image-based powder measurement (IBPM) technique, whose illumination light is a cylindrical light, was used to acquire powder distribution characteristics. A set of data preprocessing methods were proposed to validate the developed model. Correlation analysis indicated that the matching coefficients between the measured and simulated data are above 0.99 on the axial plane of the coaxial powder stream, whereas above 0.95 on the cross-sections near the focal spot. The error of the predicted focal spot position was less than 1%. Mean sensitivity indexes inferred that both the injection radius and the injection angle of the powder-fed head have significant influence on the focal spot characteristics of the DCPS compared to the nozzle exit diameter. Reducing the focal spot diameter can improve the powder utilization efficiency. For achieving safe deposition process and stable powder delivery, the standoff distance at the focal spot should be greater than 6 mm.

Approximation Algorithms for Stochastic and Risk-Averse Optimization

We present improved approximation algorithms in stochastic optimization. We prove that the multistage stochastic versions of covering integer programs (such as set cover and vertex cover) admit essentially the same approximation algorithms as their standard (nonstochastic) counterparts; this improves upon work of Swamy and Shmoys which shows an approximability that depends multiplicatively on the number of stages. We also present approximation algorithms for facility location and some of its variants in the 2-stage recourse model, improving on previous approximation guarantees. We give a 2.2975-approximation algorithm in the standard polynomial-scenario model and an algorithm with an expected per-scenario 2.4957-approximation guarantee, which is applicable to the more general black-box distribution model.

Bayesian Inference, Monte Carlo Sampling and Operational Risk.

Operational risk is an important quantitative topic as a result of the Basel II regulatory requirements. Operational risk models need to incorporate internal and external loss data observations in combination with expert opinion surveyed from business specialists. Following the Loss Distributional Approach, this article considers three aspects of the Bayesian approach to the modelling of operational risk. Firstly we provide an overview of the Bayesian approach to operational risk, before expanding on the current literature through consideration of general families of non-conjugate severity distributions, g-and-h and GB2 distributions. Bayesian model selection is presented as an alternative to popular frequentist tests, such as Kolmogorov-Smirnov or Anderson-Darling. We present a number of examples and develop techniques for parameter estimation for general severity and frequency distribution models from a Bayesian perspective. Finally we introduce and evaluate recently developed stochastic sampling techniques and highlight their application to operational risk through the models developed.

Inverse Simulation of Heat Source in Electrical Discharge Machining (EDM)

Temperature field is the main material loading in EDM and responsible for rim zone modifications. For adjusting dedicated surface integrity, it is necessary to quantify the material loading. A direct measurement of temperatures near the working area is not possible due to very high temperatures and corresponding gradients. Also numerical prediction of temperature field is difficult, because no general model for power distribution during EDM process exists. One possibility to obtain this power distribution is inverse simulation. For given temperatures in the inner workpiece a heat source with defined power fraction can be calculated. Hence, the entire temperature field can be determined.

Effect of interference percentage on damage mechanism of carbon fiber reinforced plastics laminate during interference-fit bolt installation

The interference-fit joint of composite laminates is widely used in assembly of thin-walled components in aviation product, but the interference percentage has a significant effect on squeezed damage which may reduce structural reliability. An investigation is conducted into the in-plane stress distribution and initial damage mechanism of symmetrical carbon fiber reinforced plastics laminates during the interference-fit bolt installation process. Considering the elastic deformation of the bolt, a general stress distribution model around the interference-fit joint is established with complex potential method. The initial damage mechanism of carbon fiber reinforced plastics laminates is characterized and critical interference percentages without damage are obtained with the mixed damage criteria. The effects of ply orientation and interference percentage on damage mechanism of each individual layer are discussed. Then, extensive finite element models with USDFLD subroutine of interference fit process are used to analyze and simulate the stress distribution and squeezed damage which are validated by strain measurement and micrographs by experiments subsequently. It is observed that theoretical solutions fall within the finite element results. The matrix tensile damage occurs first, and the critical interference percentages decrease from 1.10% to 0.85% with bolt diameters varying from 4 to 10 mm.

Investigation of various characterization methods using generalized distribution model and artificial neural network

Abstract Characterization of complex mixtures is a key factor in the phase behavior study of reservoir fluids. In this work, a statistical analysis has been carried out over wide range of experimental data to investigate the accuracy of various characterization methods based on the attainable input data of heptane plus fraction (C 7+ ) using generalized distribution model. On the other hand, a new accurate approach was developed based on Riazi’s generalized distribution model with input data of molecular weight ( M 7+ ), specific gravity (SG 7+ ) and true boiling point distribution (Tb). In addition, an artificial neural network has been trained and tested for three different sets of input data including ( M 7+ ,SG 7+ ), ( M 7+ ,SG 7+ , Refractive index) and ( M 7+ ,SG 7+ ,Tb 7+ ). The last input data presented the most precise results in a good agreement with experimental data. Ultimately, the impact of characterization methods and lumping on the phase behavior for two crude oil samples was also investigated.