Statistical Checks Research Articles

Modelling the water–plant cuticular polymer matrix membrane partitioning of diverse chemicals in multiple plant species using the support vector machine-based QSAR approach

In this study, a support vector machine (SVM) based multi-species QSAR (quantitative structure–activity relationship) model was developed for predicting the water–plant cuticular polymer matrix membrane (MX) partition coefficient, KMXw of diverse chemicals using two simple molecular descriptors derived from the chemical structures and following the OECD guidelines. Accordingly, the Lycopersicon esculentum Mill. data were used to construct the QSAR model that was externally validated using three other plant species data. The diversity in chemical structures and end-points were verified using the Tanimoto similarity index and Kruskal–Wallis statistics. The predictive power of the developed QSAR model was tested through rigorous validation, deriving a wide series of statistical checks. The MLOGP was the most influential descriptor identified by the model. The model yielded a correlation (r2) of 0.966 and 0.965 in the training and test data arrays. The developed QSAR model also performed well in another three plant species (r2 > 0.955). The results suggest the appropriateness of the developed model to reliably predict the plant chemical interactions in multiple plant species and it can be a useful tool in screening the new chemical for environmental risk assessment.

Check and calibration of a single GNSS receiver by using the VRS RTN positioning method

The constant evolution of the demands of geodetic applications requires instruments, products and services with certified specifications and measurement accuracy.Particularly Global Navigation Satellite Systems (GNSS) are now closely approaching the same uncertainty as measurements provided by total stations, in current geodetic applications. In addition, the small occupation time required to measure points, leads to the increased use of them.The technological advancement of GNSS receivers and the installation of many networks of permanent and continuously operated reference stations in the world have now resulted in the use of a single GNSS receiver to determine coordinates and geometric features worldwide.This paper proposes a methodology for both, the check and calibration of a single GNSS receiver, using the VRS method, which is subset of the network RTK positioning methods (from now on mentioned as RTN methods).In both procedures the appropriate statistical checks were carried out in order to conclude about the proper function of the receiver under check.The methodology succeeds the results by using efficient number and type of observations and simple mathematical models. So it is convenient to be used by professionals in order to improve and ensure their products. Moreover it may consists as a new supplement of the ISO, as the existing ISO 17,123-8 (ISO 17123-8, 2007) requires two GNSS receivers in order to be implemented by using the real time kinematic method. It is worth mentioned that ISO 17,123-8 informs the user only for the internal accuracy, namely the precision of the base receiver which is under check.Therefore the proposed methodology concludes not only about the precision of a single GNSS receiver being checked, but also about the provided accuracy due to the comparison with the true values.

Spatio-temporal dynamics of a planktonic system and chlorophyll distribution in a 2D spatial domain: matching model and data

Field data on chlorophyll distribution are investigated in a two-dimensional spatial domain of the Mediterranean Sea by using for phytoplankton abundances an advection-diffusion-reaction model, which includes real values for physical and biological variables. The study exploits indeed hydrological and nutrients data acquired in situ, and includes intraspecific competition for limiting factors, i.e. light intensity and phosphate concentration. As a result, the model allows to analyze how both the velocity field of marine currents and the two components of turbulent diffusivity affect the spatial distributions of phytoplankton abundances in the Modified Atlantic Water, the upper layer of the water column of the Mediterranean Sea. Specifically, the spatio-temporal dynamics of four phytoplankton populations, responsible for about 80% of the total chlorophyll a, are reproduced. Results for phytoplankton abundances obtained by the model are converted in chlorophyll a concentrations and compared with field data collected in twelve marine sites along the Cape Passero (Sicily)- Misurata (Libya) transect. Statistical checks indicate a good agreement between theoretical and experimental distributions of chlorophyll concentration. The study can be extended to predict the spatio-temporal behaviour of the primary production, and to prevent the consequent decline of some fish species in the Mediterranean Sea.

In Silico Strategy for Diagnosis of Chronic Kidney Disease

Chronic kidney disease (CKD) is the third deadliest reason for mortality worldwide. An early detection of CKD would help to decelerate the loss of kidney function. Computational approaches provide opportunities to screen large populations for diagnosis of CKD. In this study, qualitative and quantitative models were developed to discriminate CKD and non-CKD subjects and to predict serum creatinine (SC) levels in populations using three simple clinical attributes as the predictors. The models were rigorously validated using stringent statistical coefficients, and applicability domains were also determined. The qualitative models yielded a binary classification accuracy >94% in test data, whereas, the quantitative models rendered a correlation (R2) of >0.94 in the test data. Values of all the statistical checks were within their respective thresholds, thus putting a high confidence in the proposed models. The proposed models can be used as the tools for screening large populations for their renal status.

The Nuts‐and‐Bolts of Publishing in the BJIR II: Evaluation Criteria

The Nuts‐and‐Bolts of Publishing in the BJIR II: Evaluation Criteria

The role of noise on the steady state distributions of phytoplankton populations

The spatio-temporal behaviour of total chlorophyll concentration is investigated in the middle of the Tyrrhenian Sea by using a stochastic approach. The study is based on a reaction–diffusion–taxis model, which is used to analyse the dynamics of five phytoplankton groups, responsible for about 80% of the total chlorophyll a inside the euphotic zone of the water column. The analysis is performed by considering: (i) the intraspecific competition of the phytoplanktonic groups for limiting factors, i.e. light intensity and nutrient concentration, (ii) the seasonal changes of environmental variables, and (iii) the random fluctuations of the components of the velocity field and temperature. Specifically, we investigate the effects of external perturbations, both deterministic and random, on the dynamics of phytoplankton populations, by inserting a term of multiplicative noise into the differential equation of the nutrient dynamics. The theoretical results of the phytoplankton abundances obtained by the stochastic model are converted in chlorophyll a concentrations, and compared with the experimental findings. The statistical checks, based on the chi-square test, show that the vertical distributions of total chlorophyll concentration are in a good agreement with the experimental data. Finally, we observe that the high intensity of environmental noise strongly modifies the steady spatial distributions of two phytoplankton groups usually localized in deeper layers, causing algal blooms in surface water.

Modeling the reactivities of hydroxyl radical and ozone towards atmospheric organic chemicals using quantitative structure-reactivity relationship approaches.

The persistence and the removal of organic chemicals from the atmosphere are largely determined by their reactions with the OH radical and O3. Experimental determinations of the kinetic rate constants of OH and O3 with a large number of chemicals are tedious and resource intensive and development of computational approaches has widely been advocated. Recently, ensemble machine learning (EML) methods have emerged as unbiased tools to establish relationship between independent and dependent variables having a nonlinear dependence. In this study, EML-based, temperature-dependent quantitative structure-reactivity relationship (QSRR) models have been developed for predicting the kinetic rate constants for OH (kOH) and O3 (kO3) reactions with diverse chemicals. Structural diversity of chemicals was evaluated using a Tanimoto similarity index. The generalization and prediction abilities of the constructed models were established through rigorous internal and external validation performed employing statistical checks. In test data, the EML QSRR models yielded correlation (R (2)) of ≥0.91 between the measured and the predicted reactivities. The applicability domains of the constructed models were determined using methods based on descriptors range, Euclidean distance, leverage, and standardization approaches. The prediction accuracies for the higher reactivity compounds were relatively better than those of the low reactivity compounds. Proposed EML QSRR models performed well and outperformed the previous reports. The proposed QSRR models can make predictions of rate constants at different temperatures. The proposed models can be useful tools in predicting the reactivities of chemicals towards OH radical and O3 in the atmosphere.

A New Class of Quality Controls for Micrometeorological Data in Complex Tropical Environments

AbstractQuality control is a particularly demanding problem for micrometeorological studies in complex environments. With the transition to electronic sensing and storage of climate data in high temporal resolution, traditional approaches of homogenization are insufficient for addressing the small-scale variability and spatial heterogeneity of the data. This problem can be successfully addressed by introducing a new class of control procedures based on the physical and climatological relations between different climate variables. The new approach utilizes knowledge about the interdependency of air temperature, precipitation, radiation, relative air humidity, cloud cover, and visibility to develop empirical functions for determining the probability margins for the co-occurrence of specific conditions in tropical mountains and deserts. It can also be applied to other geographic settings by adjusting the parameters derived from the data itself. All procedures are integrated into a processing chain with feedback loops and combined with conventional logical and statistical checks, which enables it to detect small errors that normally pass unnoticed. The algorithms are also adapted to incorporate the short time steps of the original data to retain the potential for detailed process analyses.

Perfect simulation of M/G/c queues

In this paper we describe a perfect simulation algorithm for the stable M/G/c queue. Sigman (2011) showed how to build a dominated coupling-from-the-past algorithm for perfect simulation of the super-stable M/G/c queue operating under first-come-first-served discipline. Sigman's method used a dominating process provided by the corresponding M/G/1 queue (using Wolff's sample path monotonicity, which applies when service durations are coupled in order of initiation of service). The method exploited the fact that the workload process for the M/G/1 queue remains the same under different queueing disciplines, in particular under the processor sharing discipline, for which a dynamic reversibility property holds. We generalise Sigman's construction to the stable case by comparing the M/G/c queue to a copy run under random assignment. This allows us to produce a naïve perfect simulation algorithm based on running the dominating process back to the time it first empties. We also construct a more efficient algorithm that uses sandwiching by lower and upper processes constructed as coupled M/G/c queues started respectively from the empty state and the state of the M/G/c queue under random assignment. A careful analysis shows that appropriate ordering relationships can still be maintained, so long as service durations continue to be coupled in order of initiation of service. We summarise statistical checks of simulation output, and demonstrate that the mean run-time is finite so long as the second moment of the service duration distribution is finite.

Perfect simulation of M/G/c queues

In this paper we describe a perfect simulation algorithm for the stable M/G/cqueue. Sigman (2011) showed how to build a dominated coupling-from-the-past algorithm for perfect simulation of the super-stable M/G/cqueue operating under first-come-first-served discipline. Sigman's method used a dominating process provided by the corresponding M/G/1 queue (using Wolff's sample path monotonicity, which applies when service durations are coupled in order of initiation of service). The method exploited the fact that the workload process for the M/G/1 queue remains the same under different queueing disciplines, in particular under the processor sharing discipline, for which a dynamic reversibility property holds. We generalise Sigman's construction to the stable case by comparing the M/G/cqueue to a copy run under random assignment. This allows us to produce a naïve perfect simulation algorithm based on running the dominating process back to the time it first empties. We also construct a more efficient algorithm that uses sandwiching by lower and upper processes constructed as coupled M/G/cqueues started respectively from the empty state and the state of the M/G/cqueue under random assignment. A careful analysis shows that appropriate ordering relationships can still be maintained, so long as service durations continue to be coupled in order of initiation of service. We summarise statistical checks of simulation output, and demonstrate that the mean run-time is finite so long as the second moment of the service duration distribution is finite.

The Paradoxes of Word of Mouth in Electronic Commerce

Challenging conventional wisdom, we unravel three paradoxes of word of mouth (WOM) in e-commerce. Specifically, the WOM valence paradox contends that higher WOM valence of a product results in a larger subsequent decrease in the WOM valence of the product, the WOM volume paradox propounds that higher WOM volume of a product results in a smaller subsequent increase in the WOM volume of the product, and the WOM spillover paradox proposes that an improvement in the WOM of a product also improves the WOM of connected products in a product network. These paradoxes caution online retailers that superior WOM may at times backfire and not boost further sales. Drawing theoretical support from expectation-confirmation theory and network theory, we collect data from China’s largest business-to-consumer platform, Tmall.com, and use linear panel data models to examine WOM evolution in a product network, controlling for relevant factors at the individual product, product network, and time unit levels. Importantly, we base our identification strategies on the use of instrumental variables and the difference-in-differences estimation approach. Numerous statistical checks confirm the robustness and consistency of our findings. We contribute to a much richer theoretical understanding of WOM, by extending the applicability of expectation-confirmation theory and network theory to novel predictions and contexts, adding a dynamic perspective, unveiling three important WOM paradoxes, and offering practical insights.

Modeling the binding affinity of structurally diverse industrial chemicals to carbon using the artificial intelligence approaches.

Binding affinity of chemical to carbon is an important characteristic as it finds vast industrial applications. Experimental determination of the adsorption capacity of diverse chemicals onto carbon is both time and resource intensive, and development of computational approaches has widely been advocated. In this study, artificial intelligence (AI)-based ten different qualitative and quantitative structure-property relationship (QSPR) models (MLPN, RBFN, PNN/GRNN, CCN, SVM, GEP, GMDH, SDT, DTF, DTB) were established for the prediction of the adsorption capacity of structurally diverse chemicals to activated carbon following the OECD guidelines. Structural diversity of the chemicals and nonlinear dependence in the data were evaluated using the Tanimoto similarity index and Brock-Dechert-Scheinkman statistics. The generalization and prediction abilities of the constructed models were established through rigorous internal and external validation procedures performed employing a wide series of statistical checks. In complete dataset, the qualitative models rendered classification accuracies between 97.04 and 99.93%, while the quantitative models yielded correlation (R(2)) values of 0.877-0.977 between the measured and the predicted endpoint values. The quantitative prediction accuracies for the higher molecular weight (MW) compounds (class 4) were relatively better than those for the low MW compounds. Both in the qualitative and quantitative models, the Polarizability was the most influential descriptor. Structural alerts responsible for the extreme adsorption behavior of the compounds were identified. Higher number of carbon and presence of higher halogens in a molecule rendered higher binding affinity. Proposed QSPR models performed well and outperformed the previous reports. A relatively better performance of the ensemble learning models (DTF, DTB) may be attributed to the strengths of the bagging and boosting algorithms which enhance the predictive accuracies. The proposed AI models can be useful tools in screening the chemicals for their binding affinities toward carbon for their safe management.

Predicting Toxicities of Diverse Chemical Pesticides in Multiple Avian Species Using Tree-Based QSAR Approaches for Regulatory Purposes.

A comprehensive safety evaluation of chemicals should require toxicity assessment in both the aquatic and terrestrial test species. Due to the application practices and nature of chemical pesticides, the avian toxicity testing is considered as an essential requirement in the risk assessment process. In this study, tree-based multispecies QSAR (quantitative-structure activity relationship) models were constructed for predicting the avian toxicity of pesticides using a set of nine descriptors derived directly from the chemical structures and following the OECD guidelines. Accordingly, the Bobwhite quail toxicity data was used to construct the QSAR models (SDT, DTF, DTB) and were externally validated using the toxicity data in four other test species (Mallard duck, Ring-necked pheasant, Japanese quail, House sparrow). Prior to the model development, the diversity in the chemical structures and end-point were verified. The external predictive power of the QSAR models was tested through rigorous validation deriving a wide series of statistical checks. Intercorrelation analysis and PCA methods provided information on the association of the molecular descriptors related to MW and topology. The S36 and MW were the most influential descriptors identified by DTF and DTB models. The DTF and DTB performed better than the SDT model and yielded a correlation (R(2)) of 0.945 and 0.966 between the measured and predicted toxicity values in test data array. Both these models also performed well in four other test species (R(2) > 0.918). ChemoTyper was used to identify the substructure alerts responsible for the avian toxicity. The results suggest for the appropriateness of the developed QSAR models to reliably predict the toxicity of pesticides in multiple avian test species and can be useful tools in screening the new chemical pesticides for regulatory purposes.

Two Species with an Unusual Combination of Traits Dominate Responses of British Grasshoppers and Crickets to Environmental Change.

There are large variations in the responses of species to the environmental changes of recent decades, heightening interest in whether their traits may explain inter-specific differences in range expansions and contractions. Using a long-term distributional dataset, we calculated range changes of grasshoppers and crickets in Britain between the 1980s and the 2000s and assessed whether their traits (resource use, life history, dispersal ability, geographic location) explain relative performance of different species. Our analysis showed large changes in the distributions of some species, and we found a positive relationship between three traits and range change: ranges tended to increase for habitat generalists, species that oviposit in the vegetation above ground, and for those with a southerly distribution. These findings accord well with the nature of environmental changes over this period (climatic warming; reductions in the diversity and increases in the height of vegetation). However, the trait effects applied mainly to just two species, Conocephalus discolor and Metrioptera roeselii, which had shown the greatest range increases. Once they were omitted from the analysis, trait effects were no longer statistically significant. Previous studies on these two species emphasised wing-length dimorphism as the key to their success, resulting in a high phenotypic plasticity of dispersal and evolutionary-ecological feedback at their expanding range margins. This, combined with our results, suggests that an unusual combination of traits have enabled these two species to undertake extremely rapid responses to recent environmental changes. The fact that our results are dominated by two species only became apparent through cautious testing of the results’ robustness, not through standard statistical checks. We conclude that trait-based analyses may contribute to the assessment of species responses to environmental change and provide insights into underlying mechanisms, but results need to be interpreted with caution and may have limited predictive power.

The Promise of Corporate Entrepreneurship: A Review of Data Analytic Strategies

A burgeoning literature on corporate entrepreneurship has emerged since the early 1990s drawing on theoretical and empirical streams in strategic management and attributes of entrepreneurship such as decision-making under Knightian uncertainty, individual level theories of creativity and exploration, and Schumpeterian notions of creative destruction. The field has enticed increasing numbers of researchers to examine promised notions of competitive advantage, wealth creation, growth, innovation, and firm performance. This paper reviews the statistical micro-foundations of the domain to ascertain whether or not the conceptual and empirical interest in corporate entrepreneurship is outpacing the quality of data analytic methodologies and statistical checks for robustness and validity. The review focuses on the era of blossoming in literature growth between 2000 and 2009 and concludes that while there is always room for improvement, that the statistical and analytical health of the research being performed is headed in a promising direction. This bodes well for the future of both inquiry and practice in this domain and the realization of its espoused benefits.

Application of Response Surface Method as an Experimental Design to Optimize Clarification Process Parameters for Sugarcane Juice

In this study, the response surface method and experimental design were applied for optimization of independent variables for clarification of sugarcane juice. A central composite design, with 6 axial points, 8 factorial points and 6 replicates at the centre point were used to build a model for predicting and optimizing clarification process parameters. The independent parameters viz Temperature (73.59, 77, 82, 87 and 90.41°C), Activated charcoal thickness (0.83, 1.0, and 1.25, 1.5 and 1.67 mm) and deola (0.063, 0.2, 0.4, 0.6 and 0.74 g/l) were selected and analyzed. Statistical checks (ANOVA table, R2 value, model lack of fit test and F-value) indicating that the model was adequate for representing the experimental data. The dependent parameters measured were viscosity, °Brix, and total solids values. Optimum values of process parameters obtained by using simultaneous optimization of dependent parameters were: 77.55°C temperature, 1.5 mm activated charcoal thickness and 0.48 g/lit deola.

QSTR modeling for qualitative and quantitative toxicity predictions of diverse chemical pesticides in honey bee for regulatory purposes.

Pesticides are designed toxic chemicals for specific purposes and can harm nontarget species as well. The honey bee is considered a nontarget test species for toxicity evaluation of chemicals. Global QSTR (quantitative structure-toxicity relationship) models were established for qualitative and quantitative toxicity prediction of pesticides in honey bee (Apis mellifera) based on the experimental toxicity data of 237 structurally diverse pesticides. Structural diversity of the chemical pesticides and nonlinear dependence in the toxicity data were evaluated using the Tanimoto similarity index and Brock-Dechert-Scheinkman statistics. Probabilistic neural network (PNN) and generalized regression neural network (GRNN) QSTR models were constructed for classification (two and four categories) and function optimization problems using the toxicity end point in honey bees. The predictive power of the QSTR models was tested through rigorous validation performed using the internal and external procedures employing a wide series of statistical checks. In complete data, the PNN-QSTR model rendered a classification accuracy of 96.62% (two-category) and 95.57% (four-category), while the GRNN-QSTR model yielded a correlation (R(2)) of 0.841 between the measured and predicted toxicity values with a mean squared error (MSE) of 0.22. The results suggest the appropriateness of the developed QSTR models for reliably predicting qualitative and quantitative toxicities of pesticides in honey bee. Both the PNN and GRNN based QSTR models constructed here can be useful tools in predicting the qualitative and quantitative toxicities of the new chemical pesticides for regulatory purposes.

Optimal design of nonwoven air filter media: Effect of fibre shape

In this work, a series of needle-punched nonwoven filter media was prepared by using polyester fibres of three different cross-sections (circular, trilobal, and deep-groove) in accordance with a three-component augmented simplex lattice design. The experimental data of filtration efficiency and pressure drop were analyzed by means of response surface methodology. Statistical model equations were developed for filtration efficiency and pressure drop by using Design-Expert® software. The filtration efficiency and pressure drop were expressed as linear functions of proportion of fibres of different shapes. Statistical checks (ANOVA, R 2 and p-value) indicated that these models were adequate for representing the experimental data. By means of contour plots, the effect of filter constituents on filtration performance was analysed. The filter media consisting of deep-grooved fibres exhibited highest filtration efficiency but at the cost of highest pressure drop. On the other hand, the filter media consisting of circular fibres displayed lowest pressure drop but at the cost of lowest filtration efficiency. As a compromise for simultaneously achieving maximum filtration efficiency of 61.52 % and minimum pressure drop of 13.6 Pa, the optimum mixture was predicted to consist of 53.7 % deep grooved fibres and 46.3 % circular fibres. The predicted response was found in close agreement with the experimental data. This demonstrates the effectiveness of the approach reported here for achieving good predictions, while minimizing number of experiments.

Experimental sloshing pressure impacts in ensemble domain: Transient and stationary statistical characteristics

The present paper focuses on the analysis of impact pressure registrations from repeated model scale sloshing experiments under harmonic rotational excitation. A series of more than 100 experiments, each one encompassing more than 100 impact events, has been conducted seeking the highest feasible repeatability. Different excitation periods, that cover the main features of the impact dynamics, have been considered in a preliminary screening, describing the main features of the impact dynamics. Since, even under a nominally deterministic excitation, the pressure at each impact is characterized by a high variability, a statistical approach is used treating the impact pressure as a stochastic process. For one selected excitation period, the statistical analysis focuses on the ensemble distribution of the maximum pressure during each impact event. Particular attention is given to the evolution of such distributions, in order to detect the variations in the statistical characteristics of the process. This is achieved by, first, identifying the presence and the length of the transient phase and, second, by characterizing the process at stationary state. The statistics of impact pressure for different peaks are discussed mostly in the ensemble domain. Linking the latter with the time domain analysis is made by checking that the problem can be considered “practically ergodic.” The “practical ergodicity” of the process is dealt with by checking to what extent steady state ensemble statistical information can be obtained from a single long run experiment. Statistical checks for correlation and independence of maximum impact pressures are also carried out to test the hypothesis of independent identically distributed random variables. The method of analysis presented in this paper through the considered example case is general in nature and is considered to be highly portable. In particular, it is considered to allow for a more thorough understanding of non-deterministic events such as those considered herein, by looking at them from a sound statistical perspective. The thorough description of the whole experimental setup makes the presented data suitable for comparison purposes and for validation of theoretical/numerical approaches.

Introduction of “Guideline for Radiation Shielding Evaluation of Transport Casks by Monte Carlo Method” -sample calculation-2: NFT-14P wet type cask-

Introduction of “Guideline for Radiation Shielding Evaluation of Transport Casks by Monte Carlo Method” is planned in Japan by National Maritime Research Institute (NMRI) because so for Monte Carlo codes are not popular to use for these kind of shielding analysis. Target of this Guideline is transport casks loading high level radioactive materials such as spent fuel. With a view to preparing the backup data to support the Guideline, we perform the radiation shielding calculation by using MCNP code for NFT-14P wet type cask which is the representative spent fuel cask of Japan. The measurements of dose rate for this cask were performed in 2009 by NMRI. The measurement results for neutrons and gamma rays for the cask loaded 14 PWR spent fuels were shown. All calculations pass the relative deviation criteria. With regard to 10 statistical checks provided by the MCNP output, some articles are not satisfied with the criteria in this study. However, the calculated dose rate obtained by the point detector and the track length estimator are in good agreement. In addition, in comparison with the measurements, the axial distribution profiles of the dose rate are in good agreement. These trends represent that the calculated dose rate are acceptable as a reasonable analysis result. In many cases, the calculated dose rate is larger than the measured dose rate, and therefore the calculation procedure presented in the Guideline is conservative and useful for the purpose of the safety analysis.