Simple Empirical Model Research Articles

Link quality estimation for arbitrary packet sizes over wireless links using packet reception events

SummaryThe packet error rate of wireless links is known to increase with the length of packets. Yet, packet length is rarely taken into account in protocols and algorithms estimating the packet error rate. Still, it is an important factor that higher layer protocols need to be aware of. In this article, we systematically measure the relationship between packet length and packet error rate over a wide range of wireless links and technologies. On the basis of our measurements, we propose a simple empirical model that can capture this behavior. Using this model, multiple methods are proposed that can estimate the packet error rate for any packet length by sampling the link. We consider methods based on hello packets with controlled packet lengths as well as data packets, where the transmitted packet lengths cannot be controlled. We investigate the accuracy of the different estimation methods in various situations and show how they are able to predict the delivery ratio for different packet sizes.

Estimating and Examining the Sensitivity of Different Vegetation Indices to Fractions of Vegetation Cover at Different Scaling Grids for Early Stage Acacia Plantation Forests Using a Fixed-Wing UAS

Understanding the information on land conditions and especially green vegetation cover is important for monitoring ecosystem dynamics. The fraction of vegetation cover (FVC) is a key variable that can be used to observe vegetation cover trends. Conventionally, satellite data are utilized to compute these variables, although computations in regions such as the tropics can limit the amount of available observation information due to frequent cloud coverage. Unmanned aerial systems (UASs) have become increasingly prominent in recent research and can remotely sense using the same methods as satellites but at a lower altitude. UASs are not limited by clouds and have a much higher resolution. This study utilizes a UAS to determine the emerging trends for FVC estimates at an industrial plantation site in Indonesia, which utilizes fast-growing Acacia trees that can rapidly change the land conditions. First, the UAS was utilized to collect high-resolution RGB imagery and multispectral images for the study area. The data were used to develop general land use/land cover (LULC) information for the site. Multispectral data were converted to various vegetation indices, and within the determined resolution grid (5, 10, 30 and 60 m), the fraction of each LULC type was analyzed for its correlation between the different vegetation indices (Vis). Finally, a simple empirical model was developed to estimate the FVC from the UAS data. The results show the correlation between the FVC (acacias) and different Vis ranging from R2 = 0.66–0.74, 0.76–0.8, 0.84–0.89 and 0.93–0.94 for 5, 10, 30 and 60 m grid resolutions, respectively. This study indicates that UAS-based FVC estimations can be used for observing fast-growing acacia trees at a fine scale resolution, which may assist current restoration programs in Indonesia.

A Simple Empirical Model for the Radar Backscatters of Skewed Sea Surfaces at X- and Ku-Bands

A Simple Empirical Model for the Radar Backscatters of Skewed Sea Surfaces at X- and Ku-Bands

Energy Decomposition Analysis of Protein-Ligand Interactions Using Molecules-in-Molecules Fragmentation-Based Method.

Accurate prediction of protein-ligand binding affinities and their quantitative decomposition into residue-specific contributions represent challenging problems in drug discovery. While quantum mechanical (QM) methods can provide an accurate description of such interactions, the associated computational cost is normally prohibitive for broad-based applications. Recently, we have shown that QM-based protein-ligand interaction energies in the gas phase can be determined accurately using our multilayer molecules-in-molecules (MIM) fragmentation-based method at a significantly lower computational cost. In this paper, we present a new approach for calculating protein-ligand interactions using our three-layer model (MIM3) that allows us to decompose the total binding affinity into quantitative contributions from individual residues (or backbone and side chain), crystal water molecules, solvation energy, and entropy. In our approach, the desolvation energy and entropy changes during protein-ligand binding are modeled using simple and inexpensive empirical models while intermolecular interactions are computed using an accurate QM method. The performance of our approach has been assessed on a congeneric series of 22 thrombin inhibitors, all with experimentally known binding affinities, using a binding pocket cutout of 120 residues with more than 1550 atoms. Comparison of our MIM3-calculated binding affinities calculated at the B97-D3BJ/6-311++G(2d,2p) level with experiment shows a good correlation with an R2 range of 0.81-0.88 and a Spearman rank correlation coefficient (ρ) range of 0.84-0.89 while providing a quantitative description of residue-specific interactions. We show that such residue-specific interaction energies can be employed to identify and rationalize both obvious (e.g., hydrogen bonds, π···π) and nonobvious (e.g., CH···π) interactions that play a critical role in protein-ligand binding. We suggest that such quantitative information can be used to identify the key residues that determine the comparative binding affinities of different ligands in order to improve and optimize the effectiveness of computational drug design.

Time‐Effective Accelerated Cyclic Aging Analysis of Lithium‐Ion Batteries

AbstractWe propose a time‐effective framework for accelerated cyclic aging analysis of lithium‐ion batteries. The proposed framework involves the coupling of a physico‐chemical capacity‐fade model that considers the cyclic aging mechanisms of the LiMn2O4/graphite cell, with a physics‐based porous‐composite electrode model to predict cycling performance at different temperatures. A one‐dimensional simple empirical life model is then developed from the coupled physico‐chemical capacity‐fade model and the physics‐based porous‐composite electrode model predictions. An accelerated cyclic aging analysis based on the principle of time‐temperature superposition is performed using the developed one‐dimensional simple life empirical model. The proposed framework is used to predict the maximum number of cycles and the highest temperature required for accelerated cyclic aging analysis of LiMn2O4/graphite cells. The efficacy of the proposed framework is validated with experimental cycle‐performance data obtained from LiMn2O4/graphite coin cells at 25 and 60 °C.

Composite Strips with U-Shaped CFRP Wrap Anchor Systems for Strengthening Reinforced Concrete Beams

The contribution of carbon fiber reinforced polymer (CFRP) wrap as an anchorage system of CFRP plates, used for flexural strengthening, is investigated. Eighteen concrete beams (100 × 150 × 1400 mm3) were cast and cured for 28 days before strengthening with both CFRP plates (1.2 × 50 mm2) and anchorage-used, U-shaped CFRP wraps. These were adhered on the top of the CFRP plates at different longitudinal and transverse extensions. The mechanical response of the concrete beams was examined under four-point loading including measurements of load versus deflection, bond stress versus slippage and strain in CFRP plates. Crack formation and failure modes were also monitored and characterized. End anchorage regimes, using 300-mm-long U-shaped segmented CFRP wraps, impart the best improvement to load capacity, rigidity, and toughness (up to 80, 57, and 40%, respectively) without harming tangibly ductility in terms of deflection at failure. Anchorage cases involving attaching continuous single or double layers of U-shaped CFRP wrap on the top of CFRP plates impart lower but tangible overall improvements in mechanical performance of CFRP-strengthened beams. A simple empirical model is developed to predict ultimate strain in CFRP plate based on present results before examining using available literature data. Moreover, the efficiency of the present technique is compared to that present in literature.

Linking below‐surface horizontal pore velocity profiles in porous media with near‐surface wind conditions and porous medium gas permeability

AbstractAbove‐surface mean wind speed profiles and corresponding below‐surface horizontal pore air velocity profiles were measured for two porous media with different particle size and gas permeability under six different wind speed conditions. Experiments followed a 2k factorial design to minimize the number of required experiments. Below‐surface pore air velocity profiles were measured using a newly developed gas tracer tracking method applied at different elevations below the surface. Results suggested that pore air velocity decreases exponentially with decreasing elevation. A simple empirical exponential model was therefore proposed to approximate the pore air velocity–elevation relationship. The pore‐velocity model was chosen such that it, together with an existing, well‐known, semi‐empirical wind‐speed profile model commonly used for describing wind speed in the atmospheric boundary layer, is able to approximate the entire air velocity profile above and below the surface. The pore air velocity profile is described by two empirical parameters that depend on the air velocity at the surface and the shape of the pore velocity profile. The links between the velocity profile shape (via the two empirical parameters), porous medium gas permeability and above‐surface wind speed conditions were analyzed based on the experimental data. Results indicate that below‐surface horizontal pore velocity profiles are strongly related to both above‐surface wind conditions and porous medium characteristics.Highlights Horizontal air velocity profiles above and below porous medium surfaces were measured A novel approach for approximating wind‐induced, horizontal pore velocity was proposed Medium and wind speed characteristics strongly affected pore velocity profiles Key parameters were: gas permeability > mean wind speed > wind gust frequency

Excitation and ionization of iron in argon and neon glow discharges: towards the true picture

A systematic detailed analysis of calibrated Fe I, Fe II emission spectra from an argon- and a neon glow discharge was performed, involving a large number of Fe I and Fe II levels. The relevant excitation and ionization processes of iron in those discharges are described and discussed, based on experimental population functions of Fe I and transition rate diagrams of Fe II in both discharges. The Fe I spectrum reflects electron impact excitation occurring at conditions far from local thermodynamic equilibrium. Some processes involving heavy particle collisions were considered also as potentially responsible for the features observed in the Fe I population function. Charge transfer between neutral iron atoms and the ions of the discharge gas is the prevailing ionization mechanism of iron in both discharges and the decay of the charge transfer-excited ionic levels dominates the Fe II spectrum. In a neon discharge, the decay proceeds step-wise, causing a massive cascade excitation of lower Fe II levels on the way. Emission associated with electron impact excitation of ground state iron ions was also observed. A simple semi-quantitative empirical collisional-radiative model is presented for iron atoms and ions in an argon glow discharge.

Systematic Design-of-Experiments, factorial-design approaches for tuning simple empirical water models

ABSTRACT Using systematic Design-of-Experiments, factorial-design approaches, we identify how five variables (site charge, sigma, epsilon, OH bond length and HOH bond angle) affect the diffusivity and radial distribution function accuracy within the framework of the Simple Point Charge water model. From this (by a full-functional DoX-effects model), we glean response equations for the effects of these systematic variations on both estimation of site-site radial distribution functions, in addition to self-diffusivities, allowing scope to estimate more precisely optimal parameters for use in tailored force-fields for particular purposes (e.g. improved fidelity towards particular desired structural or dynamical properties). To this end, the diffusivity error was reduced from an initial value of 35.2–0.64%, whilst the radial distribution function was more difficult to improve, with a reduction in error of 2.83%.

Inhibiting effect of coal fly ash on minimum ignition temperature of coal dust clouds

The inhibiting effect of coal fly ash on the combustion of a coal dust cloud was studied for three typical types of coal: anthracite, bituminous coal, and lignite. Using a Godbert–Greenwald furnace, the minimum ignition temperature (MIT) of coal dust clouds containing various quantities of coal fly ash was measured. The results show that the MIT of coal dust clouds increases linearly with increasing quantities of coal fly ash. Compared with calcium carbonate as a common inhibitor, coal fly ash was found to have a better inhibiting effect owing to its larger heat capacity. Furthermore, at the same concentration of coal fly ash, the larger the coal dust particle size, the higher the MIT of the coal dust cloud. Moreover, at the same concentration of coal fly ash, the higher the content of volatile matter in the coal dust, the lower the MIT of the coal dust cloud. Coal fly ash has the strongest inhibiting effect on lignite, the second strongest inhibiting effect on bituminous coal, and the weakest inhibiting effect on anthracite. Combining all the factors examined in this work, a simple empirical model is presented that provides a reasonable estimation of MIT.

Effect of damage incubation in the laser grooving of sapphire

With the advancement of ultrashort pulsed-laser processing technologies, greater control of processing conditions has come into demand. A factor which particularly complicates ablation situations is “damage incubation,” a phenomenon in which the intrinsic optical properties of the processed material change due to accumulated defects from repeated laser excitation. Damage incubation can induce striking changes in the observed morphology during ablation and should be an important factor governing processing results. However, only a few studies have incorporated these effects into multiple-pulse ablation models due to its complexity. Here, in order to quantify the effects of damage incubation in a practical processing setting, we study ablation morphologies of shallow grooves formed on the surface of sapphire (α-Al2O3) with varying laser pulse number and energy in a purpose-made experiment. We observe clear evidence of incubation-induced changes in ablation phase and nonlinear dependence of depth on the incident total energy density. To understand the results, we create a simple empirical model for material energy absorption by characterizing interpulse absorption changes and analytically derive solutions for two limiting cases in which the material has either a very low (quasistatic absorption) or very high (accumulative absorption) damage incubation characteristic. By following the energy absorption characteristics predicted by the latter model, we were able to derive universal relations between ablated depth and incident energy density for sapphire. This work serves to highlight the effects of damage incubation on multiple-pulse ablation situations and provides a simple and practical method to predict such morphological characteristics of an arbitrary material.

Seasonal change of CH4/H2 ratio in the atmosphere of Uranus

Uranus is mainly composed of elements heavier than hydrogen and helium, such as C, S, even though very little information about these elements has been detected. Methane is one of the most important component of the atmosphere of Uranus. Analysis of the full-disk spectra of Uranus, as observed by the Xinglong 2.16m telescope with BFOSC (Beijing-Faint Object Spectrograph and Camera) on August 26 2016, was performed. A simple empirical model was used to fit the several years of spectrum albedo data to determine the variations of the CH4/H2 ratio in the atmosphere of Uranus. These data include: spectra observed by the 1.52m telescope (1994 and 1995) on La Silla, Chile; spectra observed by the HST-STIS (2002 and 2003); and spectra observed by the Xinglong 2.16m telescope (2016). The interannual change of the CH4/H2 ratio can be derived by comparing the fit results of various spectral albedos. The comparative results indicate that the CH4/H2 ratio in the atmosphere of Uranus declined from 1994 until 2002, and then began to rise thereafter. From the results, we speculate that the variation period of CH4/H2 ratio in the atmosphere of Uranus is longer than twenty years. We were unable to confirm whether the period is 84 or 42 years.

Magnetically tunable optical diffraction gratings based on a ferromagnetic liquid crystal.

Transmission optical diffraction gratings composed of periodic slices of a ferromagnetic liquid crystal and a conventional photoresist polymer are demonstrated. Dependence of diffraction efficiencies of various diffraction orders on an in-plane external magnetic field is investigated. It is shown that diffraction properties can be effectively tuned by magnetic fields as low as a few mT. The tuning mechanism is explained in the framework of a simple empirical model and also by numerical simulations based on the rigorous coupled wave analysis (RCWA). The obtained results provide a proof of principle of operation of magnetically tunable liquid crystalline diffractive optical elements applicable in contactless schemes for control of optical signals.

Empirical prediction of traffic noise transmission loss across plenum windows

A parametric study on the traffic noise transmission loss across plenum windows was carried out experimentally in this investigation in an attempt to establish a simple empirical model for predicting this transmission loss. A total of fourteen full scale plenum windows were included in this study. The results of a site mockup measurement were used for model validation. The present model was developed based on the existing plenum chamber theory in which the sound fields inside the plenum window cavities were assumed to make up of a diffracted wave and a reverberant field. Results suggest that both the diffracted and reverberant field inside the plenum window cavities are weaker than those assumed in existing plenum chamber theory. It is found that a model, which assumes frequency-independent diffraction directivity and percentage reverberant field attenuation, gives the best prediction of traffic noise transmission loss. This prediction model is also able to predict site test results with good accuracy.

Phenomenological models as effective tools to discover cellular design principles.

Microbes have proved useful to us in many different ways. To utilize microbes, we have mostly focused on maximizing growth, to improve yield of chemicals derived from the microbes. However, to truly tap into their potential, we should also aim to understand microbial physiology. We present a historical perspective of the developments in the field of Microbial Biotechnology, focusing on how the growth-modelling approaches have changed. Starting from simple empirical growth models, we have evolved towards mechanistic and phenomenological models which use molecular and physiological details to drastically improve prediction power of these models. Lastly, we explore the as of yet unsolved questions in microbial physiology, and discuss how the ability to monitor microbial growth at single cell resolution using the lab-on-a-chip technologies is uncovering previously unobservable causal principles underlying microbial growth.

Computational methods for describing acoustic propagation in forests

Acoustic propagation through stands of trees, both large and small, can be markedly different from propagation through an open environment. Ground properties, meteorology, and significant scattering effects all play a role. An understanding of these effects, and computational methods used to describe them, can be utilized in applications ranging from noise mitigation to wildlife communication. This tutorial explores the myriad of computational methods that have been developed to describe acoustic propagation in forested environments. Beginning with the early simple empirical models describing attenuation, the presentation then narrows the field of view to contributions of individual components within a forest. Models for describing scattering by trunks and foliage, both individually and as ensembles, are presented. Next, the integration of these individual components into the fully coupled system that includes ground properties and meteorology, within computational methods such as the parabolic equation (PE) and finite-difference, time-domain (FDTD) methods are shown. Finally, examples are presented showing how computational methods for forest acoustics can be used for evaluating noise mitigation strategies and wildlife studies. Acoustic propagation through stands of trees, both large and small, can be markedly different from propagation through an open environment. Ground properties, meteorology, and significant scattering effects all play a role. An understanding of these effects, and computational methods used to describe them, can be utilized in applications ranging from noise mitigation to wildlife communication. This tutorial explores the myriad of computational methods that have been developed to describe acoustic propagation in forested environments. Beginning with the early simple empirical models describing attenuation, the presentation then narrows the field of view to contributions of individual components within a forest. Models for describing scattering by trunks and foliage, both individually and as ensembles, are presented. Next, the integration of these individual components into the fully coupled system that includes ground properties and meteorology, within computational methods such as the parabolic equation (...

From abstract to concrete: some tips for developing an empirical stock–flow consistent model

The main purpose of this paper is to show how a simple (medium-scale) empirical stock–flow consistent dynamic model can be developed from scratch. Eurostat data and conventional statistical packages (notably EViews, Excel and R) are used. On the theoretical side, the work builds upon the pioneering work of Godley/Lavoie (2007). Sectoral transactions–flow matrices and balance sheets are explicitly modelled and their evolution over time under different scenarios is analysed. On the empirical side, the model draws upon the applied work of Burgess et al. (2016). The case of Italy is considered, but the model can be replicated for other countries. Eurostat annual data (from 1995 to 2016) are used to estimate or calibrate most model parameter values (for example, consumption function and housing investment parameters). Remaining parameters are borrowed from the available literature or taken from a range of realistic values (for example, weight on past errors in agents’ expectations). The model is then used to impose and compare alternative scenarios for Italian sectoral financial balances, based on different shocks to government spending.

Tackling the challenges in the estimation of methane absolute adsorption in kerogen nanoporous media from molecular and analytical approaches

Accurate characterization of methane absolute adsorption in shale nanoporous media is of great importance to the gas-in-place (GIP) estimation and well productivity. Because experimental measurement can only provide the excess adsorption, the absolute adsorption is generally converted from the excess adsorption based on the single-layer adsorption model. However, it is well known that shale has a widespread pore size distribution (PSD), ranging from sub 2-nm to hundreds of nanometers. In micropores (<2 nm), methane may have layering structures, which deviates from the commonly used adsorption model. Thus, it is necessary to take into account the varying methane adsorption behavior in micropores and mesopores and consider the PSD effect to obtain the absolute adsorption from the experimentally measured excess adsorption. In this work, we propose a number of artificially generated PSDs and study methane adsorption in each nanopore by using grand canonical Monte Carlo (GCMC) simulations. By coupling GCMC simulations and varying PSDs, we effectively model methane adsorption in nanoporous media.Based on the varying density profiles in different nanopores obtained from GCMC, we propose the corresponding methane adsorption model in each nanopore, which is applied in Ono-Kondo (OK) lattice model. By fitting the excess adsorption in nanoporous media and explicitly considering the PSD, OK model can readily obtain the absolute adsorption. In order to validate our model, 1000 sets of randomly generated PSDs are used. We find that our proposed OK model has an excellent agreement with GCMC simulation, while the commonly used method to convert the excess adsorption to the absolute adsorption without considering the PSD shows noticeable deviations. Moreover, the optimized constant adsorbed phase densities are very different from the commonly used values as 424 kg/m3 and 373 kg/m3. Our work proposes a simple, efficient and accurate empirical model to obtain the absolute adsorption in nanoporous media. This work should provide important insights into accurate characterization methane absolute adsorption and the gas-in-place estimation in shale.

Household characteristics at the bottom, the typical, and the top of the 2016 income distribution in South Korea: A quantile regression analysis

AbstractThis paper seeks understanding how demographic characteristics affect the different quantiles of the South Korean income distribution. Findings from the 2016 Household Income and Expenditure Survey data suggest that, at the bottom of the distribution, households relied heavily on cash transfers from institutions and other households, while those at the top relied more on wages, suggesting that high‐income households in South Korea were not members of the “propertied class.” We then develop simple empirical models to measure the effect of demographic covariates on household income conditional on a number of variables commonly used in inequality studies, including lifecycle, education, gender, marital status, urban residence, job security, industry and occupation type. Quantile regression results demonstrate how a particular quantile of the conditional distribution changes with demographic characteristics. Specifically, we found an increase in gender and unemployment effects as we go from high to low quantiles. Programmes aimed at combating gender discrimination and persistent unemployment therefore are expected to be inequality reducing. This is in contrast to policies to promote higher education or to expand public sector employment that quantile regression models indicate to be inequality enhancing.

A simple and reliable empirical model with two predictors for estimating 1-minute diffuse fraction

Recent progress in radiometry allows the development of diffuse fraction models based on data stored at high-frequency sampling. At the present only a handful of such models exist in the literature. In this paper, we present a new and simple model for estimating diffuse fraction developed on 1-minute resolution data.The new model (PB) is extensively validated on data from the Baseline Surface Radiation Network, for both diffuse fraction (kd) and direct normal irradiance (Gn). The results show that PB performs well compared to other similar models, despite its structural simplicity. kd is estimated with the best accuracy in locations with temperate climate (TM), the average (median) nRMSE of the tested TM stations being 18.38% (17.79%). The Gn estimates are found to be most accurate at arid locations (AR), the average (median) nRMSE over all tested AR stations being 17.29% (16.96%). We find that the performance of the models varies strongly as a function of the climate zone or when we switch between estimating diffuse and direct normal irradiance components, possibly changing the accuracy-based rankings among models, a fact which should be taken into consideration by users when selecting appropriate models.