Variability Model Research Articles

On-Wafer Drain Current Variability in GaN MIS-HEMT on 200-mm Silicon Substrates

In this study, a detailed on-wafer (or global) variability analysis of drain current characteristics of GaN MIS-HEMT devices grown on 200 mm silicon substrate is conducted. For the first time to our knowledge, the on-wafer variability sources in GaN technologies due to the manufacturing process are investigated by combining experimental data and analytical variability modeling. The key parameters which affect the variability are oxide the interface charge fluctuations, the mobility fluctuations, the gate oxide thickness and/or the gate area variations and the access resistance fluctuations in the contact as well as in the 2DEG regions (source and drain sides). Due the specificity of GaN MIS HEMT device engineering process, we show that their variability performances are not, for the time being, comparable to the state-of-the art silicon CMOS technologies, and this can be valuable for reliable improvement and optimization of GaN technology fabrication process. This study has been verified over a large range of channel gate lengths for three normally-off GaN MIS-HEMT wafers and having different gate process flows.

Link Between the Time‐Space Behavior of Rainfall and 3D Dynamical Structures of Equatorial Waves in Global Convection‐Permitting Simulations

AbstractEquatorial waves (EWs) control a considerable portion of tropical rainfall variability but numerical models often struggle to capture them. Increased computing power now enables global simulations with resolved deep convection, which is believed to produce more realistic EWs. Here we identify EWs in global ICON simulations with varying horizontal resolution by (a) filtering rainfall based on space‐time spectral analysis and (b) projecting wind and geopotential onto theoretical wave patterns. The simulations demonstrate that Kelvin, mixed‐Rossby gravity and equatorial Rossby waves are consistently represented, regardless of model resolution and convective treatment. For smaller‐scale inertio‐gravity waves, however, explicit convection appears to be a prerequisite. Surprisingly, the associated rainfall signals are not accompanied by corresponding wind patterns but appear to be connected to mesoscale convective systems. This demonstrates the importance of analyzing rainfall and dynamical aspects of EWs jointly for a robust assessment.

Stochastic Modelling of Spatial Variability of Petrophysical Properties of Zingo Field Niger Delta Basin Nigeria

This research is focused on the, stochastic modeling of spatial variability of petrophysical properties in reservoir sands in thirteen oil wells of Zingo Field, Niger Delta Basin, Nigeria. Petrophysical properties of the reservoirs sands was generated using well logs, Archie’s equation with Pickett plot and Timur (1968) version of Wyllie and Rose empirical equation. Fault sticks and pillar gridding were used to delineate structural features and generate top surfaces as interpreted horizons on the seismic sections. Results from the stochastic modeling shows that the reservoirs exhibit very good qualities with 17% and 15% volume of shale, 27% and 26% total porosity, 458mD and 565mD permeability for reservoirs C5000 and C6000 respectively. Reservoir C5000 main structures are dipping NE-SW and consists of interpreted major NW-SE fault and other faults trending NE-SW. The research shows that an understanding and properly capturing the reservoir geometry, quality and distribution is important in estimating in place hydrocarbon volumes.

Mapping soil pH levels across Europe: An analysis of LUCAS topsoil data using random forest kriging (RFK)

AbstractSoil pH affects food production, pollution control and ecosystem services. Mapping soil pH levels, therefore, provides policymakers with crucial information for developing sustainable soil use and management policies. In this study, we used the LUCAS 2015 TOPSOIL data to map soil pH at a European level. We used random forest kriging (RFK) to build a predictive model of spatial variability of soil pH, as well as random forest (RF) without co‐kriging and boosted regression trees (BRT) modelling techniques. Model accuracy was evaluated using a ten‐fold cross‐validation procedure. While we found that all methods accurately predicted soil pH, the accuracy of the RFK method was best with regression performance metrics of: R2 = 0.81 for pH (H2O) and pH (CaCl2); RMSE = 0.59 for pH (H2O) and RMSE = 0.61 in pH (CaCl2); MAE = 0.41 for pH (H2O) and MAE = 0.43 in pH (CaCl2). Dominant explanatory variables in the RF and BRT modelling were topography and remote sensing variables, respectively. The generated maps broadly depicted similar spatial patterns of soil pH, with an increasing gradient of soil pH from north to south Europe, with the highest values mainly concentrated along the Mediterranean coast. The mapping could provide spatial reference for soil pH assessment and dynamic monitoring.

Enhanced evolutionary approach for solving fractional difference recurrent neural network systems: A comprehensive review and state of the art in view of time-scale analysis

<abstract><p>The present research deals with a novel three-dimensional fractional difference neural network model within undamped oscillations. Both the frequency and the amplitude of movements in equilibrium are subsequently estimated mathematically for such structures. According to the stability assessment, the thresholds of the fractional order were determined where bifurcations happen, and an assortment of fluctuations bifurcate within an insignificant equilibrium state. For such discrete fractional-order connections, the parameterized spectrum of undamped resonances is also predicted, and the periodicity and strength of variations are calculated computationally and numerically. Several qualitative techniques, including the Lyapunov exponent, phase depictions, bifurcation illustrations, the $ 0-1 $ analysis and the approximate entropy technique, have been presented with the rigorous analysis. These outcomes indicate that the suggested discrete fractional neural network model has crucial as well as complicated dynamic features that have been affected by the model's variability, both in commensurate and incommensurate cases. Furthermore, the approximation entropy verification and $ \mathbb{C}_{0} $ procedure are used to assess variability and confirm the emergence of chaos. Ultimately, irregular controllers for preserving and synchronizing the suggested framework are highlighted.</p></abstract>

Seismic vulnerability of fixed-base and base-isolated hospitals: blind comparison between shaking table and numerical tests

New hospitals are frequently designed using base-isolation systems to improve their structural response in future seismic events, contrary to the observed rather poor seismic performance of several fixed-base hospitals. Despite this evidence, a fair question arises about the convenience, or not, of using these modern seismic protection technologies as opposed to other conventional structural design solutions, on the grounds of a holistic approach based on structural and nonstructural damage expected as well as continuity of service to the community in the aftermath of an earthquake. The current study aims to an integrated numerical assessment in a hospital setting, with particular attention to evaluating and classifying functionality of nonstructural components and critical medical equipment. Two scaled four-storey (fixed-base) and three-storey (base-isolated) hospital framed buildings were built and subjected to shaking table tests at the University of Kyoto (Japan). A two-phase experimental campaign with numerical structural and nonstructural blind prediction took place, considering two earthquakes scaled at different intensity levels. To this end, a self-built C++ code is developed to account for lumped plasticity modelling of steel frame members and variability of the friction coefficient of curved surface sliding bearings. Moreover, three contest nonstructural components are modelled in the fixed-base structure: i.e. elastic single degree of freedom systems representing two tanks filled with sand (top floor); elastic beam elements for piping (third floor); five-element macro-model for the in-plane-out-of-plane nonlinear response of partition walls (first floor). Finally, a self-built MATLAB code is employed in order to analyse sliding, rocking and jumping motion of three contest medical equipment (i.e. incubator at third floor; dialysis machine at second floor; surgical bed at first floor), on the basis of acceleration time histories of selected structural nodes of the fixed-base structure.

Improving spatial and temporal variation of ammonia emissions for the Netherlands using livestock housing information and a Sentinel-2-derived crop map

Ammonia emissions to the atmosphere have a range of negative impacts on environmental quality, human health, and biodiversity. Despite the considerable efforts in quantifying spatially explicit ammonia emissions, there are significant uncertainties in ammonia emission estimates at regional scales. We aimed to improve the modeling of atmospheric ammonia emission variability in space and time across the Netherlands by updating an agricultural ammonia emission model with a newly derived high-resolution crop map and a livestock housing location database of the Netherlands. To generate a crop map of 12 agricultural land cover classes, we applied random forest classification to the multi-temporal multispectral observations of surface reflectance and vegetation indices derived from Sentinel-2. The crop statistics were used to calculate ammonia emission distribution based on nitrogen demand (manure and mineral fertilizer needed) of different crop types using the INTEGRATOR model. Next, the crop map was utilized to spatially allocate the ammonia emissions to a high-resolution grid across the Netherlands. In addition, ammonia emissions from livestock housing systems were introduced as point sources using location data from the Geographic Information Agricultural Business system. The temporal emission variability was updated using a recently developed TIMELINES module. After the spatial and temporal distribution of ammonia emission was obtained with the crop map and housing information, it was imported into the chemistry transport model LOTOS-EUROS to model ammonia surface concentration for validation with in situ measurements. The performed crop classification has an average accuracy score of 0.73. The derived crop map was compared with Dutch national statistics, and the results showed that the absolute median of the relative difference between Sentinel-2 derived crop areas and national statistical information is around 5%. The newly modeled ammonia monthly surface concentrations compared better with in situ measurements in terms of the magnitude and temporal variability than those derived from the original emission distribution, indicating that the temporal distribution of ammonia emissions was improved. The comparison of modeled and measured annual averaged surface concentrations illustrated that the spatial distribution of ammonia emission was also improved. All model performance indicators significantly improved, and the performance of the updated model was more stable and robust. The improvement was more evident at the stations where livestock housing is the main emission source. This study illustrates that apart from a satellite-derived crop map, information on the locations of animal housing systems also plays an essential role in better estimates of the spatial and temporal distribution of ammonia emissions. It can be worthwhile to extrapolate the method to other regions in Europe and elsewhere.

МОДЕЛИРОВАНИЕ ПРОСТРАНСТВЕННО-ВРЕМЕННОЙ ИЗМЕНЧИВОСТИ АГРОКЛИМАТИЧЕСКИХ РЕСУРСОВ ЮГА ТЮМЕНСКОЙ ОБЛАСТИ

The article presents the results of studying the agro-climatic characteristics of the south of the Tyumen region. Based on the data, a cartographic model of spatio-temporal variability of the sum of effective temperatures, precipitation and hydrothermal coefficient was constructed. Despite the fact that in recent years, the agro-climatic resources of the region enable farmers to grow non-traditional crops for the region, and in other regions of the country, scientists record climate warming, our studies have established a decrease in the sum of temperatures during the growing season in the southern and middle zones by 150–200 °С. The time periods of 2003–2012 and 2013–2022 show that over the past 10 years in the south of the Tyumen region there has been only a change in the nature of the spatial distribution of heat supply and humidification conditions. Due to the decrease in the level of evaporation, the increase in moisture supply and the hydrothermal coefficient have shifted to the north-west of the region.

Predicting central incisor length from the incisive papilla in a multiethnic Northern Nigerian population

ABSTRACT Background: Personalization of complete dentures (CDs) may improve their acceptance by patients. This study sought to develop an individualized predictive model for central incisor length in CDs based on the relationship of the incisive papilla with central incisors. Methods: Seventy-three adult participants were included in the study. The vertical distance (VD) and horizontal distance (HD) from the incisive papilla to the central incisor margins and the mean crown heights (CHs) of both central incisors were determined and recorded along with potential confounders. Bivariate and multivariate analyses were performed to develop a best-fit model equation for predicting the CH from the VD and HD. Results: Thirty-seven males and 36 females (mean age: 33.4 ± 7.8 years) participated in the study. The ovoid arch form (61.6%) was the most common, whereas the square-shaped arch form (4.1%) was the least common. The mean CH, HD, and VD were 10.12 ± 1.02 mm, 10.64 ± 1.36 mm, and 8.00 ± 1.19 mm, respectively. Further, 49% of CH variability (Model 1) was explained by HD (β = 0.61, t = 7.02, P < 0.001) and VD (β = 0.29, t = 3.29, P = 0.002). Participant sex (Model 2) explained another 3% CH variability (β =0.18, t = 2.09, P = 0.04). The predictive equation from Model 2 was CH (mm) = 3.28+ (0.46 × HD) + (0.22 × VD) +0.37 × participant sex. Conclusion: The central incisor CH can be predicted with 52% accuracy using the equation CH (mm) =3.28 + (0.46 × HD) + (0.22 × VD) + 0.37 × participant sex. This finding may aid the fabrication of more individualized dentures.

The Distributions of Uncalibrated Speaker Verification Scores: A Generative Model for Domain Mismatch and Trial-Dependent Calibration

Speaker verification systems that compute log-likelihood ratios (LLR) between the same and different speaker hypotheses allow for cost-effective decisions that depend only on prior information. Domain mismatch, inaccurate model assumptions or the intrinsic nature of non-probabilistic classifiers often result in mis-calibrated scores, and a re-calibration step is required to map the classifier outputs to well-calibrated LLRs. Standard calibration is based on Logistic Regression, often paired with quality measures to provide trial-dependent calibration transformations. More recently, generative methods have been proposed as an alternative to discriminative approaches, which, however, are not yet able to exploit additional side information. In this work we introduce a novel generative approach based on the analysis of the effects of speaker vector distribution mismatch on the distribution of verification scores for PLDA and PLDA-based classifiers. We show that target and non-target scores can be modeled by Variance-Gamma distributions, whose parameters represent effective between and within-class variability. This allows us to introduce utterance-dependent variability models that can incorporate both explicit quality measures, such as the utterance duration, or implicit measures, such as the norm of a speaker embedding. Experimental results on different test sets with different front-ends and classifiers show that the proposed approach improves both calibration and verification accuracy with respect to state-of-the-art calibration models.

Oscillations of fluid tori around neutron stars

AbstractWe examine the influence of quadrupole moment of a slowly rotating neutron star (NS) on the oscillations of a fluid accretion disk (torus) orbiting a compact object the spacetime around which is described by the Hartle‐Thorne geometry. Explicit formulae for non‐geodesic orbital epicyclic and precession frequencies, as well as their simplified practical versions that allow for an expeditious application of the universal relations determining the NS properties, are obtained and examined. We demonstrate that the difference in the accretion disk precession frequencies for NSs of the same mass and angular momentum, but different oblateness, can reach up to tens of percent. Even higher differences can arise when NSs with the same mass and rotational frequency, but different equations of state (EoS), are considered. In particular, the Lense‐Thirring precession frequency defined in the innermost parts of the accretion region can differ by more than one order of magnitude across NSs with different EoS. Our results have clear implications for models of the LMXBs variability.

Overview of NASA’s Solar Irradiance Science Team #2 (SIST‐2) Program

AbstractThe Solar Irradiance Science Team (SIST) #2 (SIST‐2) program is a competitively solicited National Aeronautics and Space Administration (NASA) Earth Science Division science research program providing 3‐year awards beginning in July 2018 to quantify and understand the solar irradiance variability and implications for Earth's climate and atmospheric composition. The purpose for the SIST‐2 program is limited to the accurate specification of the incoming solar irradiance into the Earth system considering the 44‐year satellite data record as well as proxies to which the satellite record can be tied. The SIST‐2 program funded eight research grants to study the variability of the total solar irradiance and solar spectral irradiance and to develop improved space‐based data sets, solar proxies, and variability models of the solar irradiance. The SIST‐2 projects are briefly introduced.

A modular metamodel and refactoring rules to achieve software product line interoperability

Emergent application domains, such as cyber–physical systems, edge computing or industry 4.0. present a high variability in software and hardware infrastructures. However, no single variability modeling language supports all language extensions required by these application domains (i.e., attributes, group cardinalities, clonables, complex constraints). This limitation is an open challenge that should be tackled by the software engineering field, and specifically by the software product line (SPL) community. A possible solution could be to define a completely new language, but this has a high cost in terms of adoption time and development of new tools. A more viable alternative is the definition of refactoring and specialization rules that allow interoperability between existing variability languages. However, with this approach, these rules cannot be reused across languages because each language uses a different set of modeling concepts and a different concrete syntax. Our approach relies on a modular and extensible metamodel that defines a common abstract syntax for existing variability modeling extensions. We map existing feature modeling languages in the SPL community to our common abstract syntax. Using our abstract syntax, we define refactoring rules at the language construct level that help to achieve interoperability between variability modeling languages.

Precision of subnational forest AGB estimates within the Peruvian Amazonia using a global biomass map

• Reproducible approach for AGB estimation from global biomass maps and NFI data. • Hybrid-inference accounted for uncertainties in map-plot data integration. • Within-block variability was the main source of uncertainty. • The use of a global biomass map slightly increased the precision of AGB. • The modest gain in precision was due to small map-plot correlations. National forest inventories (NFI) provide essential forest-related biomass and carbon information for country greenhouse gas (GHG) accounting systems. Several tropical countries struggle to execute their NFIs while the extent to which space-based global information on aboveground biomass (AGB) can support national GHG accounting is under investigation. We assess whether the use of a global AGB map as auxiliary information produces a gain in precision of subnational AGB estimates for the Peruvian Amazonia. We used model-assisted estimators with data from the country’s NFI and explored hybrid inferential techniques to account for the sources of uncertainty associated with the integration of remote sensing-based products and NFI plot data. Our results show that the selected global biomass map tends to overestimate AGB values across the Peruvian Amazonia. For most strata, directly using the map in its published form did not reduce the precision of AGB estimates. However, after calibrating the map using the NFI data, the precision of our map-assisted AGB estimates increased by up to 50% at stratum level and 20% at Amazonia level. We further demonstrate how different sources of uncertainties can be incorporated in the map-NFI integrated estimates. With the hybrid inferential analysis, we found that the small spatial support of the NFI plots compared to the remote sensing-based sample units of aggregated pixels (within block variability) contributed the most to the total uncertainty associated with the AGB estimates from our map-NFI integration. Uncertainties caused by measurement variability and allometric model prediction uncertainty were the second largest contributors. When these uncertainties were incorporated, the increase in precision of our calibrated map-assisted AGB estimates was negligible, probably hindered by the great contribution of the within block variability to our map-plot assessment. We developed a reproducible method that countries can build upon and further improve while the global biomass products continue to evolve and better characterize the AGB distribution under large biomass conditions. We encourage further cross-country case studies that reflect a wider range of AGB distributions, especially within humid tropical forests, to further assess the contribution of global biomass maps to (sub)national AGB estimates and finally GHG monitoring and reporting.

Extreme variability modelling of overdispersed germination count experiments

Extreme variability modelling of overdispersed germination count experiments

Yielding and Ultimate Deformations of Wide and Deep Reinforced Concrete Beams

Current formulations proposed by Eurocode 8 part 3 for the inelastic deformations of existing reinforced concrete members are assessed separately for wide beams (WB) and conventional deep beams (DB). The current approach, based on a large experimental database of members, predicts larger ultimate chord rotation but lower chord rotation ductility for WB rather than for DB despite the similar curvature ductility, due to lower plastic hinge lengths in WB. However, if the data are disaggregated into DB and WB, predicted chord rotations are consistently conservative for DB and not conservative for WB if compared with experimental values, especially at ultimate deformation. Thus, plastic hinge length may be even greater for DB in comparison to WB. Therefore, some feasible corrections of the formulations for chord rotations are proposed, in order to reduce the bias and thus increase the robustness of the model for cross-section shape variability.

A comprehensive Pelgrom-based on-current variability model for FinFET, NWFET and NSFET

We present a novel Pelgrom-based predictive (PBP) model to estimate the impact of variability on the on-current of different state-of-the-art semiconductor devices. In this work, we focus on two of the most problematic sources of variability, the metal grain granularity (MGG) and the line edge roughness (LER). This model allows us to make an accurate prediction of the on-current standard deviation σIon, being the relative error of the predicted data lower than 8% in 92% of the studied cases. The PBP model entails an immense reduction in the computational cost since once it is calibrated for an architecture, the prediction of the impact of a variability on devices with any given dimension can be made without any further simulations. This model could be useful for predicting the effect of variability on future technology nodes.

A Spatially Varying Ground Motion Model with an Evolving Energy Spectrum

Besides phase variability, amplitude variability is one of the two manifestations of the spatially varying ground motion (SVGM) in the frequency domain. Neglecting the amplitude variability of the earthquake spectra can result in an underestimation of the structural responses. Few existing amplitude variability models can be used for estimating spectra at a distance from a reference location. In this paper, a new amplitude variability model describing the evolution of the energy spectra is developed based on records of five earthquake events acquired from the SMART 1 array. The similarity between the spectra of two locations is used as a metric for measuring the spectrum changes. The energy spectra at a distance from a reference location are found to be composed of two parts, including the averaged spectra and the random variation part. In the new model, the former is estimated by the moving average of the reference spectrum, while the envelope of the latter is approximated by the wavelet approximation of the reference spectrum’s Fourier amplitude spectrum. The parameters of five models for each event and a general model for all five events are identified. Monte Carlo simulations are used for testing the models. The results validate the new model in terms of capturing the similarity changes of actual earthquakes.

Local empirical model of ionospheric variability

The paper presents an analysis of the ionospheric variability as a function of local time, month, and geomagnetic activity level. The 2003–2020 dataset of peak electron densities (NmF2) from the Irkutsk DPS-4 Digisonde (52.3°N, 104.3°E) was converted into the dataset of the NmF2 disturbances (ΔNmF2) representing the relative (percentage) deviations of the NmF2 from the 27-day running median. The ΔNmF2 dataset was used to calculate root mean square values of ΔNmF2 (σNmF2) by 27-day running averaging. These σNmF2 values were considered as a measure of ionospheric variability. The σNmF2 as function of local time, day of year, and year was the input for building the local empirical model of ionospheric variability based on the linear regression of σNmF2 on the 27-day average daily Ap-index of geomagnetic activity. The paper demonstrates the diurnal-seasonal variations in σNmF2 under low geomagnetic activity (linear regression intercept) as well as the rate of increase/decrease in σNmF2 with increasing Ap (linear regression slope). The obtained diurnal, seasonal, and geomagnetic activity behavior of σNmF2 is compared with previous studies of ionospheric variability.

Geostatistical Modelling of Soil Spatial Variability by Fusing Drone-Based Multispectral Data, Ground-Based Hyperspectral and Sample Data with Change of Support

Traditional soil characterization methods are time consuming, laborious and invasive and do not allow for long-term repeatability of measurements. The overall aim of this paper was to assess and model spatial variability of the soil in an olive grove in south Italy by using data from two sensors of different types: a multi-spectral on-board drone radiometer and a hyperspectral visible-near infrared-shortwave infrared (VIS-NIR-SWIR) reflectance radiometer, as well as sample data, to arrive at a delineation of homogeneous areas. The hyperspectral data were processed using Continuum Removal (CR) methodology to obtain information about the content and composition of clay. Differently, the multispectral data were firstly upscaled to the support of soil data using geostatistics and taking into account the change of support. Secondly, the data acquired with the two different sensors were integrated with soil granulometric properties by using two multivariate geostatistical techniques: multi-collocated cokriging to achieve a more exhaustive and finer-scale soil characterization, and multi-collocated factor cokriging to extract synthetic scale-dependent indices (regionalized factors) for the delineation of soil in homogeneous zones. This paper shows the impact of change of support on the uncertainty of soil prediction that can have a significant effect on decision making in Precision Agriculture. Moreover, four regionalized factors at two different scales (two for each scale) were retained and mapped. Each factor provided a different delineation of the field with areas characterized by different granulometries and clay compositions. The applied method is sufficiently flexible and could be applied to any number and type of sensors.