Empirical Statistical Models Research Articles

Re-evaluation of the rabbit myosin protein standard used to create the empirical statistical model for decoy library searching

A Rabbit myosin standard, like that used to create the empirical statistical model, was randomly and independently sampled by liquid chromatography micro electrospray ionization and tandem mass spectrometry (LC-ESI-MS/MS) with a linear quadrupole ion trap. The rabbit myosin protein standard appeared pure by SDS-PAGE and CBBR staining but showed many other proteins by silver staining. The LC-MS intensity from myosin and IgG samples were above the 99% safe limit of detection and quantification computed from 36 blank LC-ESI-MS/MS runs. The myosin contained ≤406 Gene Symbols, open reading frames or loci where 79 protein types showed ≥3 peptides from X!TANDEM. Myosins, actin, troponin, other proteins showed 95%–100% homology between the rabbit versus the human decoy library. The myosin protein complex from STRING was true positive compared to random or noise spectra MS/MS with a low type I error (p-value) and low FDR (q-value) computed in R. SDS-PAGE, Western blot, comparison to random and noise MS/MS spectra, X!TANDEM p-values, FDR corrected q-values, and STRING all agreed that the error rate of LC-ESI-MS/MS with a quadrupole ion trap is far below that assumed a priori by the design of the empirical statistical model for decoy library searching.

Spatio-temporal modelling of the status of groundwater droughts

An empirical (geo)statistical modelling scheme is developed to address the challenges of modelling the severity and distribution of groundwater droughts given their spatially and temporally heterogeneous nature and given typically highly irregular groundwater level observations in space and time. The scheme is tested using GWL measurements from 948 observation boreholes across the Chalk aquifer (UK) to estimate monthly groundwater drought status from 1960 to 2013. For each borehole, monthly GWLs are simulated using empirical mixed models where the fixed effects are based on applying an impulse response function to the local monthly precipitation. Modelled GWLs are standardised using the Standardised Groundwater Index (SGI) and the monthly SGI values interpolated across the aquifer to produce spatially distributed monthly maps of SGI drought status for 54 years for the Chalk. The mixed models include fewer parameters than comparable lumped parameter groundwater models while explaining a similar proportion (more than 65%) of GWL variation. In addition, the empirical modelling approach enables confidence bounds on the predicted GWLs and SGI values to be estimated without the need for prior information about catchment or aquifer parameters. The results of the modelling scheme are illustrated for three major episodes of multi-annual drought (1975–1976; 1988–1992; 2011–2012). They agree with previous documented analyses of the groundwater droughts while providing for the first time a systematic, spatially coherent characterisation of the events. The scheme is amenable to use in near real time to provide short term forecasts of groundwater drought status if suitable forecasts of the driving meteorology are available.

Island Volcanic Ecosystems in the Pacific Mobile Belt as Sources of Continental Biosphere Formation

Empirical-statistical modeling of the structure of island-arc forest ecosystems has been performed based on data from a large-scale landscape and ecological survey in the region of the Mendeleev Volcano (Kunashir Island, the Southern Kuril Ridge) and on regional bioclimatic data. These systems characterize one of the initial stages of the formation of the continental biosphere in the Pacific mobile belt. The causes of the general lowering of the boundaries of altitudinal bioclimatic belts and the shift of natural zones to the south in the extratropical Neopacific in comparison with the adjacent continent have been determined. It has been revealed that the dark-coniferous and mixed forests of Kunashir Island differ from their continental analogs by their higher productivity, which makes them similar to subtropical forests of Northwest Pacific. The predomination of the transpiration component in the heat balance provides a high level of autotrophic biogenesis, which results in the stability of island-arc forests under unfavorable cold oceanic conditions. The hypothesis that the geothermic energy of passive volcanoes favors the formation of climatically inadequate forests and enhanced evolution of island phytobiota has been, in general, empirically substantiated.

Influence of using electroless Ni-P coated WC-Co powder on laser cladding of stainless steel

In this investigation, nickel (Ni)-coated WC-12Co powder was produced using an electroless nickel‑phosphorus (Ni-P) plating process and then used as a feedstock material for laser cladding of AISI 321 stainless steel substrate. The correlations that exist between key operational parameters of the process (i.e., laser power, scanning speed, powder feeding rate) and geometrical characteristics for single claddings (i.e. height, width, dilution, and wetting angle) were predicted and analyzed by regression analysis method (RA). A processing window for laser cladding of Ni-coated WC-12Co powder on AISI 321 stainless steel was developed based on an empirical-statistical model and the processing window was compared with that for laser cladding of commercial WC-12Co powder without nickel coating. Furthermore, the influence of using Ni-coated WC-12Co powder on the microstructure and microhardness was investigated and compared with those of laser cladded commercial WC-12Co claddings. The results showed that the use of Ni-coated WC-12Co powder could significantly reduce the porosity percentage and lead to high-quality claddings.

Can chlorophyll-a in meso-oligotrophic shallow waters be estimated using statistical approaches and empirical models from MODIS imagery?

ABSTRACT Accurate estimation of chlorophyll-a (Chl-a) concentration in inland waters through remote-sensing techniques is complicated by local differences in the optical properties of water. In this study, we applied multiple linear regression (MLR), artificial neural network (ANN), nonparametric multiplicative regression (NPMR) and four models (Appel, Kahru, FAI and O14a) to estimate the Chl -a concentration from combinations of spectral bands from the MODIS sensor. The MLR, NPMR and ANN models were calibrated and validated using in-situ Chl -a measurements. The results showed that a simple and efficient model, developed and validated through multiple linear regression analysis, offered advantages (i.e., better performance and fewer input variables) in comparison with ANN, NPMR and four models (Appel, Kahru, FAI and O14a). In addition, we observed that in a large shallow subtropical lake, where the wind and hydrodynamics are essential factors in the spatial heterogeneity (Chl-a distribution), the MLR model adjusted using the specific point dataset, performed better than using the total dataset, which suggest that would not be appropriate to generalize a single model to estimate Chl-a in these large shallow lakes from total datasets. Our approach is a useful tool to estimate Chl -a concentration in meso-oligotrophic shallow waters and corroborates the spatial heterogeneity in these ecosystems.

Symposium review: Uncertainties in enteric methane inventories, measurement techniques, and prediction models

Ruminant production systems are important contributors to anthropogenic methane (CH4) emissions, but there are large uncertainties in national and global livestock CH4 inventories. Sources of uncertainty in enteric CH4 emissions include animal inventories, feed dry matter intake (DMI), ingredient and chemical composition of the diets, and CH4 emission factors. There is also significant uncertainty associated with enteric CH4 measurements. The most widely used techniques are respiration chambers, the sulfur hexafluoride (SF6) tracer technique, and the automated head-chamber system (GreenFeed; C-Lock Inc., Rapid City, SD). All 3 methods have been successfully used in a large number of experiments with dairy or beef cattle in various environmental conditions, although studies that compare techniques have reported inconsistent results. Although different types of models have been developed to predict enteric CH4 emissions, relatively simple empirical (statistical) models have been commonly used for inventory purposes because of their broad applicability and ease of use compared with more detailed empirical and process-based mechanistic models. However, extant empirical models used to predict enteric CH4 emissions suffer from narrow spatial focus, limited observations, and limitations of the statistical technique used. Therefore, prediction models must be developed from robust data sets that can only be generated through collaboration of scientists across the world. To achieve high prediction accuracy, these data sets should encompass a wide range of diets and production systems within regions and globally. Overall, enteric CH4 prediction models are based on various animal or feed characteristic inputs but are dominated by DMI in one form or another. As a result, accurate prediction of DMI is essential for accurate prediction of livestock CH4 emissions. Analysis of a large data set of individual dairy cattle data showed that simplified enteric CH4 prediction models based on DMI alone or DMI and limited feed- or animal-related inputs can predict average CH4 emission with a similar accuracy to more complex empirical models. These simplified models can be reliably used for emission inventory purposes.

Development of Models for Charring Rate of Selected Construction Timber Species in Southwestern Nigeria

The performance of Nigeria timber species under fire exposure to prevent collapse of structure has not been adequately investigated. This study was to determine the charring rate models of some selected timber species mostly used for constructional purposes in Southwestern Nigeria. Six species out of ten identified timbers were selected for studies. They are: Afara, Iroko, Opepe, Mahogany, Mansonia, and Teak. The densities of the timber species were determined at Moisture Contents (MC) of 9.0, 12.0, and 15.0%. Samples from each of the selected species, were exposed to fire at temperature ranges of 20° to 230° C for 30 minutes; 20° to 300° C for 60 minutes; 230° to 600° C for 30 minutes. Empirical statistical model was developed for charring rate of the timber species. The models were analysed using ANOVA at . At 30 minutes fire exposure (20 to 230° C), Afara of 9.0, 12.0 and 15.0% MC had the highest mean charring rates of 0.84±0.02mm/min, 0.82±0.02mm/min and 0.82±0.02mm/min respectively, while Opepe had the lowest charring rates of 0.48±0.02 mm/min, 0.48±0.02 mm/min and 0.47±0.02 mm/min at the three MC levels. This study indicated that density was a major predictor of the charring rate of constructional timber. Opepe which had the highest density also exhibited the lowest charring rate of all the species tested and is useful and recommended to ensure the safety and comfort of occupants in case of fire outbreaks.

Assessing the environmental impacts of soil compaction in Life Cycle Assessment

Maintaining biotic capacity is of key importance with regard to global food and biomass provision. One reason for productivity loss is soil compaction. In this paper, we use a statistical empirical model to assess long-term yield losses through soil compaction in a regionalized manner, with global coverage and for different agricultural production systems. To facilitate the application of the model, we provide an extensive dataset including crop production data (with 81 crops and corresponding production systems), related machinery application, as well as regionalized soil texture and soil moisture data. Yield loss is modeled for different levels of soil depth (0–25cm, 25–40cm and >40cm depth). This is of particular relevance since compaction in topsoil is classified as reversible in the short term (approximately four years), while recovery of subsoil layers takes much longer. We derive characterization factors quantifying the future average annual yield loss as a fraction of the current yield for 100years and applicable in Life Cycle Assessment studies of agricultural production. The results show that crops requiring enhanced machinery inputs, such as potatoes, have a major influence on soil compaction and yield losses, while differences between mechanized production systems (organic and integrated production) are small. The spatial variations of soil moisture and clay content are reflected in the results showing global hotspot regions especially susceptible to soil compaction, e.g. the South of Brazil, the Caribbean Islands, Central Africa, and the Maharashtra district of India. The impacts of soil compaction can be substantial, with highest annual yield losses in the range of 0.5% (95% percentile) due to one year of potato production (cumulated over 100y this corresponds to a one-time loss of 50% of the present yield). These modeling results demonstrate the necessity for including soil compaction effects in Life Cycle Impact Assessment.

Enhanced process development using automated continuous reactors by self-optimisation algorithms and statistical empirical modelling

Reaction optimisation and understanding is fundamental for process development and is achieved using a variety of techniques. This paper explores the use of self-optimisation and experimental design as a tandem approach to reaction optimisation. A Claisen-Schmidt condensation was optimised using a branch and fit minimising algorithm, with the resulting data being used to fit a response surface model. The model was then applied to find new responses for different metrics, highlighting the most important for process development purposes.

Comparing Machine Learning and Decision Making Approaches to Forecast Long Lead Monthly Rainfall: The City of Vancouver, Canada

Estimating maximum possible rainfall is of great value for flood prediction and protection, particularly for regions, such as Canada, where urban and fluvial floods from extreme rainfalls have been known to be a major concern. In this study, a methodology is proposed to forecast real-time rainfall (with one month lead time) using different number of spatial inputs with different orders of lags. For this purpose, two types of models are used. The first one is a machine learning data driven-based model, which uses a set of hydrologic variables as inputs, and the second one is an empirical-statistical model that employs the multi-criteria decision analysis method for rainfall forecasting. The data driven model is built based on Artificial Neural Networks (ANNs), while the developed multi-criteria decision analysis model uses Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach. A comprehensive set of spatially varying climate variables, including geopotential height, sea surface temperature, sea level pressure, humidity, temperature and pressure with different orders of lags is collected to form input vectors for the forecast models. Then, a feature selection method is employed to identify the most appropriate predictors. Two sets of results from the developed models, i.e., maximum daily rainfall in each month (RMAX) and cumulative value of rainfall for each month (RCU), are considered as the target variables for forecast purpose. The results from both modeling approaches are compared using a number of evaluation criteria such as Nash-Sutcliffe Efficiency (NSE). The proposed models are applied for rainfall forecasting for a coastal area in Western Canada: Vancouver, British Columbia. Results indicate although data driven models such as ANNs work well for the simulation purpose, developed TOPSIS model considerably outperforms ANNs for the rainfall forecasting. ANNs show acceptable simulation performance during the calibration period (NSE up to 0.9) but they fail for the validation (NSE of 0.2) and forecasting (negative NSE). The TOPSIS method delivers better rainfall forecasting performance with the NSE of about 0.7. Moreover, the number of predictors that are used in the TOPSIS model are significantly less than those required by the ANNs to show an acceptable performance (7 against 47 for forecasting RCU and 6 against 32 for forecasting RMAX). Reliable and precise rainfall forecasting, with adequate lead time, benefits enhanced flood warning and decision making to reduce potential flood damages.

Estimation and inversion across the spectrum of carbon cycle modeling

Understanding of the carbon cycle is particularly important because of the role of carbon dioxide as a greenhouse gas. Carbon cycle models play an essential role in the interpretation of observational data. The analysis of the carbon cycle involves statistical estimation in various contexts. These include various types of model calibration, including the estimation of feedbacks. A range of inverse calculations are involved in estimating the spatial and/or temporal dependence of carbon dioxide sources and sinks, given observations of concentrations. The uncertainties in these estimates propagate into uncertainties in projections of future carbon cycle behavior. These disparate analyses are discussed in terms of a modeling spectrum that runs from empirical statistical models through to reductionist mechanistic models. The use of the modeling spectrum allows a comparison of di erent modeling approaches. Comparing di erent levels of modeling can provide a basis for assessing the extent to which estimation is being applied consistently.

Rapid and comprehensive discovery of unreported shellfish allergens using large-scale transcriptomic and proteomic resources

[Extract] Discovery of allergens in various food and inhalant sources is central to our understanding of the molecular mechanisms of allergic reactions. Allergen characterization is the most important underlying factor for better patient management and the design and development of novel immunotherapeutics.1 Of the "Big eight" allergen food groups, shellfish presents a unique challenge in terms of allergen discovery due to the large number and diversity of consumed species, leading to heterogeneity of allergen structure and cross-reactivity among various sources. At present, 31 crustacean allergens have been officially registered in the World Health Organization and International Union of Immunological Societies Allergen Nomenclature Database as compared with 4 mollusk allergens due to pitfalls in current allergy discovery approaches. Cosensitization of patients with crustacean and mollusk allergy is often described; however, the current diagnostic approaches to manage these patients are not based on sufficient molecular knowledge of these shellfish allergens.

Multi-directional one-handed strength assessments using AnyBody Modeling Systems

Digital human modeling tools support proactive ergonomics in optimizing work tasks and workplace layouts. Empirical-statistical model based tools are often used to estimate the force exertion capability of the operators. This work is intended to serve as an initial probing into the usability of a musculoskeletal model based software, AnyBody Modeling Systems (AMS), in evaluating the force exertion capability at different points in the workspace and for various exertion directions. As a first step, it focuses on the modeling approach and the accuracy of one-handed isometric strength estimates of AMS. An existing literature database was used to compare the predicted strength at 8 hand locations and in 26 exertion directions, while simulating the empirical postures. The results show a correlation coefficient of 0.7 between the simulated and the experimental strength. AMS emphasizes the biomechanical advantages in strength due to the alignment of force exertion direction with the shoulder. Additionally, some discrepancies have been identified and discussed.

Prediction of blasting-induced fragmentation in Meydook copper mine using empirical, statistical, and mutual information models

One of the most important aims of blasting in open pit mines is to reach desirable size of fragmentation. Prediction of fragmentation has great importance in an attempt to prevent economic drawbacks. In this study, blasting data from Meydook mine were used to study the effect of different parameters on fragmentation; 30 blast cycles performed in Meydook mine were selected to predict fragmentation where six more blast cycles are used to validate the results of developed models. In this research, mutual information (MI) method was employed to predict fragmentation. Ten parameters were considered as primary ones in the model. For the sake of comparison, Kuz-Ram empirical model and statistical modeling were also used. Coefficient of determination (R 2), root mean square error (RMSE), and mean absolute error (MAE) were then used to compare the models. Results show that MI model with values of R 2, RMSE, and MAE equals 0.81, 10.71, and 9.02, respectively, is found to have more accuracy with better performance comparing to Kuz-Ram and statistical models.

Permeability calculations in unconsolidated homogeneous sands

Permeability, k, is a property of a porous medium that measures the capacity and ability of the medium to transmit fluids. The permeability of a porous medium (porous rock, soil, packing) can be predicted using empirical relationships, capillary models, statistical models, and hydraulic radius theories. It generally has been recognized that grain size is the fundamental independent variable that controls permeability in unconsolidated sediments [Shepherd, R. G. 1989].Sub-pore scale modeling is used in this study to evaluate the relation between grain size distribution and permeability of the porous media. In this method the governing equations are applied directly on the porous medium images. The meshing algorithm is applied directly on the pore and grain sections of the images, and the studied mechanism is simulated at the level of pores and grains. Virtual porous media are generated using regular packings of uniforms spheres. The permeability of each packing is calculated computationally and then compared to the predictions of the Kozeny-Carman equation. An in-house developed pattern generator is used to generate spherical grains packing with pre-specified particle size distributions. A sub-pore scale modeling approach is used to calculate permeabilities of generated virtual porous media. The equivalent diameters that provide match from the Kozeny-Carman equation are then predicted.

Air quality modeling for accountability research: Operational, dynamic, and diagnostic evaluation

Photochemical grid models play a central role in air quality regulatory frameworks, including in air pollution accountability research, which seeks to demonstrate the extent to which regulations causally impacted emissions, air quality, and public health. There is a need, however, to develop and demonstrate appropriate practices for model application and evaluation in an accountability framework. We employ a combination of traditional and novel evaluation techniques to assess four years (2001-02, 2011-12) of simulated pollutant concentrations across a decade of major emissions reductions using the Community Multiscale Air Quality (CMAQ) model. We have grouped our assessments in three categories: Operational evaluation investigates how well CMAQ captures absolute concentrations; dynamic evaluation investigates how well CMAQ captures changes in concentrations across the decade of changing emissions; diagnostic evaluation investigates how CMAQ attributes variability in concentrations and sensitivities to emissions between meteorology and emissions, and how well this attribution compares to empirical statistical models. In this application, CMAQ captures O3 and PM2.5 concentrations and change over the decade in the Eastern United States similarly to past CMAQ applications and in line with model evaluation guidance; however, some PM2.5 species—EC, OC, and sulfate in particular—exhibit high biases in various months. CMAQ-simulated PM2.5 has a high bias in winter months and low bias in the summer, mainly due to a high bias in OC during the cold months and low bias in OC and sulfate during the summer. Simulated O3 and PM2.5 changes across the decade have normalized mean bias of less than 2.5% and 17%, respectively. Detailed comparisons suggest biased EC emissions, negative wintertime SO42− sensitivities to mobile source emissions, and incomplete capture of OC chemistry in the summer and winter. Photochemical grid model-simulated O3 and PM2.5 responses to emissions and meteorologically across the decade match results from receptor-based, statistical regression models. PM2.5 sensitivities to mobile source emissions in the summertime have decreased substantially, but wintertime mobile sensitives remain largely unchanged because decreases in negative SO42− sensitivities match decreases in positive sensitivities from other constituents. Similarly, NOX emissions have led to decreased summertime O3 and increased wintertime O3 because of opposite sensitivities. Overall, results show that emissions reductions improved air quality across the domain and remain a viable option for improving future air quality.

Offshore Wind Speed Forecasting: The Correlation between Satellite-Observed Monthly Sea Surface Temperature and Wind Speed over the Seas around the Korean Peninsula

Wind power forecasting is a key role for large-scale wind power penetration on conventional electric power systems by understanding stochastic nature of winds. This paper proposes an empirical statistical model for forecasting monthly offshore wind speeds as a function of remotely sensed sea surface temperatures over the seas around the Korean Peninsula. The model uses the optimal lagged multiple linear regression method, and predictors are characterized by mixed periodicities derived from the autocorrelation between spatially variable satellite-observed sea surface temperatures and wind speeds at all grid points over a period of about ten years (2001 to 2008). Offshore wind speeds were found to be correlated with sea surface temperatures within a seasonal range of two- to four-month lags. In particular, offshore wind speeds were closely associated with the sea surface temperature at lag 4 M, followed by lag 3 M and lag 2 M. Correlation is less at lag 1 M as compared lag 2 M, lag 3 M and lag 4 M. The results demonstrate that this approach successfully produces accurate depictions of monthly wind speeds at the gridded network. The hindcast offshore wind speeds and wind power density showed slightly improved skills compared to the seasonally varying climatology with the value of root-mean square errors, +18% and +23%, respectively. The spatial distributions of the monthly gridded wind speed and wind power density remained fairly stable from one month to another, whereas individual regions displayed slight differences in variability. The results of this study are expected to be useful in establishing guidelines for operating and managing nascent offshore farms around the Korean Peninsula.

Statistical–Empirical Modeling of Airfoil Noise Subjected to Leading-Edge Serrations

With the objective of reducing the broadband noise from the interaction of highly turbulent flow and airfoil leading edge, sinusoidal leading-edge serrations were investigated as an effective passive treatment. An extensive aeroacoustic study was performed to determine the main influences and interdependencies of factors, such as the Reynolds number, turbulence intensity, serration amplitude, and wavelength as well as the angle of attack on the noise-reduction capability. A statistical–empirical model was developed to predict the overall sound pressure level and noise reduction of a NACA 65(12)-10 airfoil with and without leading-edge serrations in the range of chord-based Reynolds numbers of . The observed main influencing factors on the noise radiation were quantified in a systematic order for the first time. Moreover, significant interdependencies of the turbulence intensity and the serration wavelength, as well as the serration wavelength and the angle of attack, were observed, validated, and quantified. The statistical–empirical model was validated against an external set of experimental data, which is shown to be accurate and reliable.

Experimental investigation of the onset of sand deposits on Hastelloy-X between 1,000°C and 1,100°C

ABSTRACTDeposit formation on turbine hardware in propulsion turbine engines can occur in many arid regions globally. Characterising crystalline deposits on metallic substrates can aid in component resilience and health monitor algorithms during particle ingestion. This study has developed two statistical empirical models for prediction from acquired experimental data for the onset of deposits. The prediction models are for crystalline particulate (Arizona Road Test Dust) deposits on a flat rectangular Hastelloy-X test coupon. Particle impingement angles varied between 20° and 80° in experimental flow temperatures of 1,000–1,100°C. Averaged deposits are methodically quantified through normalised particle deposit tallies per area and percent coverage of the surface using microscopic imaging and image processing programs. Deposit accumulation is a quadratic function of both near-surface coupon temperature and coupon angle.

Improving the retrieval of solar-induced chlorophyll fluorescence at canopy level by modeling the relative peak height of the apparent reflectance

Recently, a great number of algorithms have been used in connection with the retrieval of solar-induced chlorophyll fluorescence (SIF) with the aim of detecting the physiological status of vegetation. In these SIF retrieval algorithms, methods based on Fraunhofer line discrimination (FLD) are widely used. However, all these FLD-based methods require that assumptions are made about the shape of the real reflectance around the absorption bands. These assumptions are a significant source of error in the retrieval of SIF and mean that satisfactory results at the O2-B band cannot be obtained if the spectral resolution (SR) is relatively coarse. In order to enable more accurate SIF retrieval at the O2-B band at canopy level, we have developed an alternative solution that uses a statistical empirical model to retrieve the SIF inside the absorption band, bypassing the assumptions about the shape of the real reflectance made in the FLD-based methods. In the proposed approach, called the reflectance height method (RHM), a model of the relative peak height of the apparent reflectance using two variables including the absorption depth of the irradiance and the gradient of the smoothed reflectance around the absorption bands is constructed using simulated training samples covering the most commonly occurring vegetation conditions. Validation of the RHM using both simulations and in situ observations showed that the RHM can effectively improve the SIF retrieval accuracy at the O2-B band at canopy level compared to traditional FLD-based methods, especially for cases using sensors with relatively coarse SR (SR≥1 nm) and high signal-to-noise ratio (SNR) (SNR≥1000).