Hypothetical Data Set Research Articles

From computational high-throughput screenings to the lab: taking metal-organic frameworks out of the computer.

Metal–organic frameworks (MOFs) are one of the most researched designer materials today, as their high tunability offers scientists a wide space to imagine all kinds of possible structures. Their uniquely flexible customisability spurred the creation of hypothetical datasets and the syntheses of more than 100 000 MOFs officially reported in the Cambridge Structural Database. To scan such large numbers of MOFs, computational high-throughput screenings (HTS) have become the customary method to identify the most promising structure for a given application, and/or to spot useful structure–property relationships. However, despite all these data-mining efforts, only a fraction of HTS studies have identified synthesisable top-performing MOFs that were then further investigated in the lab. In this perspective, we review these specific cases and suggest possible steps to push future HTS more systematically towards synthesisable structures.

Estimation of population mean in presence of missing data under simple random sampling

Missing data is an obligatory phenomenon in most of the sample surveys and when it is not handled cautiously at beginning of the study, outcomes may result to substantial biases in the survey estimates. Retaining such evidences into cognizance, this paper proffers few possible improved imputation methods to figure out the problem of missing data at the starting of the study and proffers computation procedure of the population mean using simple random sampling. The characteristics of the proffered imputation methods are ascertained till first order approximation pursued by a computational study carried out using several existent and hypothetical data sets. The findings are turned out rather promising showing dominance over the existing imputation methods. Further, apposite advices have been forwarded to the survey professionals for practical applications.

Hybrid fragility curve derivation of buildings based on post-earthquake reconnaissance data

This study proposes a new hybrid method that uses both of post-earthquake reconnaissance data and numerical analysis results based on a finite element (FE) model. As the uncertainty of a capacity threshold for a structural damage state needs to be estimated carefully, in the proposed method, the probabilistic distribution parameters of capacity thresholds are evaluated based on post-earthquake reconnaissance data. Subsequently, the hybrid fragility curves were derived for several damage states using the updated distribution parameters of capacity thresholds. To illustrate the detailed process of the proposed hybrid method, it was applied to piloti-type reinforce concrete (RC) buildings which were affected by the 2017 Pohang earthquake, Korea. In the example, analytical fragility curves were derived first, and then hybrid fragility curves were obtained using the distribution parameters of capacity thresholds which were updated based on actual post-earthquake reconnaissance data about the Pohang city. The results showed that the seismic fragility estimates approached to the empirical failure probability at 0.27 g PGA, corresponding to the ground motion intensity of the Pohang earthquake. To verify the proposed method, hybrid fragility curves were derived with the hypothetical reconnaissance data sets created based on assumed distribution parameters with errors of 10% and 1%. As a result, it was identified that the distribution parameters accurately converged to the assumed parameters and the case of 1% error had better convergence than that of 10% error.

Inference and combination of missing data sets for the determination of [formula omitted] thermal decomposition rate uncertainty

The thermal decomposition of H2O2 is an important process in hydrocarbon combustion, playing a particularly crucial role in providing a source of radicals at high pressure where it controls the 3rd explosion limit in the H2-O2 system, and also as a branching reaction in intermediate-temperature hydrocarbon oxidation. As such, estimating the Arrhenius parameters of the rate expression for this reaction and their joint uncertainty is crucial for performing meaningful predictive combustion computations. Raw experimental data is typically unavailable for most reported investigations of elementary reaction rates. Rather, what is typically available are summary statistics on measured quantities. The unavoidable loss of information in the use of summary statistics versus raw data for inference makes the direct computation of the necessary joint uncertainty structure of the parameters in rate expressions difficult. To overcome this situation we perform a statistical inference procedure relying on maximum entropy principles and approximate Bayesian computation methods, and employing a two-level nested Markov Chain Monte Carlo algorithm, to approximate the joint posterior density on the H2O2 decomposition rate parameters for a selected case of laser absorption measurements in a shock tube study, subject to the constraints imposed by the rate constant statistics and available information reported for this experiment. This procedure constructs a set of hypothetical H2O2 concentration decay profiles consistent with the reported statistics in the form of mean values and error bars of rate constants at the given experiment temperatures, and the data fitting model originally employed by the experimentalists - essentially reconstructing the missing experimental data in an approximate form. These consistent hypothetical data sets are then used to determine the joint Arrhenius parameter density through straightforward Bayesian inference within a pooling procedure. We apply the method in the context of combining the information within a single experimental setting (i.e. across a temperature range for fixed pressure) for the purposes of estimating Arrhenius parameters, and then extend the analysis to incorporate information from additional experiments (at a different target pressure) resulting in a decrease in the uncertainty of the parameter estimates as this new information is employed. We also highlight the flexibility of operating in this data-centric framework with a view to generating unbiased consensus rate expressions up to the full mechanism level when using multiple sources of experimental results in different forms.

An Informatics Approach for Designing Conducting Polymers.

Doping conjugated polymers, which are potential candidates for the next generation of organic electronics, is an effective strategy for manipulating their electrical conductivity. However, selecting a suitable polymer-dopant combination is exceptionally challenging because of the vastness of the chemical, configurational, and morphological spaces one needs to search. In this work, high-performance surrogate models, trained on available experimentally measured data, are developed to predict the p-type electrical conductivity and are used to screen a large candidate hypothetical data set of more than 800 000 polymer-dopant combinations. Promising candidates are identified for synthesis and device fabrication. Additionally, new design guidelines are extracted that verify and extend knowledge on important molecular fragments that correlate to high conductivity. Conductivity prediction models are also deployed at www.polymergenome.org for broader open-access community use.

Comparison of the robust methods in the general linear regression model

We compared the robust methods in the general linear regression (GLR) model through simulations. The results show that the S-type estimators produce the minimum mean squared error (MSE) of the model in all samples and the minimum standard errors of the estimators of the regression coefficients in almost all samples in all situations including the normal distribution despite their modesty in the efficiency. As an addition to the classical efficiency concept, we introduce a new efficiency concept based on the MSE of the model and the standard errors of the estimators of the regression coefficients. The simulations show that the S-type estimators are superior in terms of the efficiencies based on the MSE of the model and the standard errors of the estimators of the regression coefficients in all situations including the normal distribution. At the end of the study we give three examples one of which using a hypothetical data set and the rest being real-life data examples. The S-type estimators produce the minimum MSE value in all examples and the minimum standard error values in most of them. The simulations and examples also reveal some interesting phenomena about the regression analysis and the estimators included in this study.

Application of the log-Logistic Model to Dose Response Relation in Microbial Risk Assessment

Microbial risk assessment in food safety is a valuable tool to reduce the risks of infection by pathogens. The dose-response relation is aimed to establish the relationship between the dose of a pathogen that populations are exposed to and the probability of the adverse health effect by the pathogen. Among many dose-response models ever proposed, the exponential and beta-Poisson models have been internationally applied, but the decision on which model is selected between them solely depends on the goodness of fit to specific data sets. On the other hands, the log-logistic model, one of the alternative models, has been little studied on the dose-response relation. In the present study, thus, the application of the log-logistic model to dose-response relation was studied with hypothetical and experimental data sets of infection (or death), comparing to the above two models. Here the experimental data sets were for pathogenic organisms such as pathogenic Escherichia coli, Listeria monocytogenes, and Cryptosporidium pavrum. Consequently, this model successfully fit to those data sets in comparison to the two models. These results suggested that log-logistic model would have the potential to apply to the dose-response relation, similar to the exponential and beta-Poisson models.

A Theory of Statistical Inference for Ensuring the Robustness of Scientific Results

Inference is the process of using facts we know to learn about facts we do not know. A theory of inference gives assumptions necessary to get from the former to the latter, along with a definition for and summary of the resulting uncertainty. Any one theory of inference is neither right nor wrong but merely an axiom that may or may not be useful. Each of the many diverse theories of inference can be valuable for certain applications. However, no existing theory of inference addresses the tendency to choose, from the range of plausible data analysis specifications consistent with prior evidence, those that inadvertently favor one’s own hypotheses. Because the biases from these choices are a growing concern across scientific fields, and in a sense the reason the scientific community was invented in the first place, we introduce a new theory of inference designed to address this critical problem. We introduce hacking intervals, which are the range of a summary statistic one may obtain given a class of possible endogenous manipulations of the data. Hacking intervals require no appeal to hypothetical data sets drawn from imaginary superpopulations. A scientific result with a small hacking interval is more robust to researcher manipulation than one with a larger interval and is often easier to interpret than a classical confidence interval. Some versions of hacking intervals turn out to be equivalent to classical confidence intervals, which means they may also provide a more intuitive and potentially more useful interpretation of classical confidence intervals. This paper was accepted by J. George Shanthikumar, big data analytics.

Multi-period multi-product closed loop supply chain network design: A relaxation approach

Growing concern about environmental deterioration, manifested in new laws to protect the environment and business competition, has stimulated the idea of Closed Loop Supply Chains (CLSC). This paper develops strategic decision-making models for two different CLSCs with multiple products over multiple periods. One of the networks has six levels, while the other has four levels, and they can be applied to products with different levels of complexity during inspection and remanufacturing operations. The supply chain networks are modeled as Integer Linear Programs (ILP) designed to assist decision-making in terms of location/allocation, transportation, and inventory. Taking advantage of the properties of the formulations, we propose an integer relaxation approach to obtain the optimal solution to ILP. The proposed models and methodologies are illustrated with case studies from the literature and with hypothetical datasets. This research contributes with design standard configurations for CLSCs that can be used for different types of products. Directly using these configurations is advantageous because the models proposed can be quickly solved using the integer relaxation approach. The associated trials confirm the utility of the models for the case studies and demonstrate encouraging results for the hypothetical datasets.

Analysis of Aggregate Production Planning Problem with Goal Programming Model

Aggregate production planning is medium range planning (3-18 months) that determines production quantities, production time management, and resources management. In the real world, companies that faced this problem usually want to optimize some objective functions that conflict each other. Therefore, the purposes of this study are to reconstruct goal programming model and to solve multiobjective aggregate production planning problem. Also, to obtain numerical solutions of production planning scenarios and weights of goal programming objective function. The model is followed by the constraint of raw material capacity, workforce level, storage space, and production capacity. The proposed planning period is six months with four objective functions to maximize profit, minimize inventory cost, minimize subcontract cost, and minimize workforce changes cost. A set of hypothetical data is used to test the validity of the proposed model with several different scenarios. The result for each scenario has been fulfilled the company’s requirement and objectives. It described through monthly quantity and mechanism of the production planning. For in-depth analysis, the effect of different weighting on target’s achievement is evaluated with 24 combinations of case.

Reflection on modern methods: causal inference considerations for heterogeneous disease etiology.

Molecular pathological epidemiology research provides information about pathogenic mechanisms. A common study goal is to evaluate whether the effects of risk factors on disease incidence vary between different disease subtypes. A popular approach to carrying out this type of research is to implement a multinomial regression in which each of the non-zero values corresponds to a bona fide disease subtype. Then, heterogeneity in the exposure effects across subtypes is examined by comparing the coefficients of the exposure between the different subtypes. In this paper, we explain why this common method potentially cannot recover causal effects, even when all confounders are measured, due to a particular type of selection bias. This bias can be explained by recognizing that the multinomial regression is equivalent to a series of logistic regressions; each compares cases of a certain subtype to the controls. We further explain how this bias arises using directed acyclic graphs and we demonstrate the potential magnitude of the bias by analysis of a hypothetical data set and by a simulation study.

Profile monitoring for count data using Poisson and Conway–Maxwell–Poisson​ regression-based control charts under multicollinearity problem

In this paper, we propose new process control charts for monitoring Poisson and Conway–Maxwell–Poisson (COM–Poisson) profiles with highly correlated variables. We first propose an iterative ridge estimator for Poisson regression under multicollinearity and then modify it to COM–Poisson regression. After we define ridge deviance residuals which are approximately normal distributed, we construct Shewhart, exponentially weighted moving average (EWMA), and cumulative sum (CUSUM) type control charts based on the ridge deviance residuals for monitoring both Poisson and COM–Poisson profiles. The performances of the proposed control charts are evaluated by using average run length criterion and the proposed control chart is compared to the existing method for monitoring the Poisson profiles through simulation study based on real historical data and COM–Poisson profile through a hypothetical data set.

A clean global production network model considering hybrid facilities

The paper presents a cleaner production and distribution model for global facility network that offers reverse logistics services under the warranty period. Hybrid manufacturing and storage facilities with flexible capacity are considered to save the extra setup and maintenance cost linked with establishing new reverse logistics facilities. The carbon emissions are accounted for the various activities related to product manufacturing and return. The carbon cap and trade policy is considered for regulating emissions. The dynamic mixed-integer non-linear programming (MINLP) model discussed in the paper optimizes the total cost of the network including costs associated with reverse logistics. The model is analysed on two hypothetical data sets. The results for the smaller data set reveal that the inventory holding is a better alternative to capacity expansion to deal with fluctuations in market demand. However, for the bigger data set, capacity expansion is recommended to satisfy the increased demand instead of relying completely on inventory management. The results are further analysed for varying carbon price and it is observed that increase in carbon price initially causes an increase in the carbon emissions but later it starts decreasing. This indicates that the increase in carbon price does not provide quick results and is more effective in controlling the emissions in the longer run. Furthermore, the proposed model investigates how different trade policies viz. free trade agreement (FTA), FTA with cap, and an increase in tariff barriers influence the total carbon emissions and the total cost. The results indicate that the total cost and the total emissions are relatively less under FTA with cap, however, there is a slight increase in the emissions of one country.

IPDmada: An R Shiny tool for analyzing and visualizing individual patient data meta-analyses of diagnostic test accuracy.

BackgroundIndividual patient data meta‐analyses (IPD‐MA) are regarded as the gold standard for systematic reviews, which also applies to systematic reviews of diagnostic test accuracy (DTA) studies. An increasing number of DTA systematic reviews with IPD‐MA have been published in recent years, but there is much variation in how these IPD‐MA were performed. A number of existing methods were found, but there is no consensus as to which methods are preferred as the standard methods for statistical analysis in DTA IPD‐MA.ObjectivesTo create a web‐based tool which integrates recommended statistical analyses for DTA IPD‐MA, and allows researchers to analyse the data and visualize the results with interactive plots.MethodsA systematic methodological review was performed to identify statistical analyses and data visualization methods used in DTA IPD‐MA. Methods were evaluated by the authors and recommended analyses were integrated into the IPDmada tool which is freely available online with the user interface developed with R Shiny package.ResultsIPDmada allows users to upload their own data, perform the meta‐analysis with both continuous and dichotomized tests, and incorporate individual level covariate‐adjusted analysis. All tables and figures can be exported as .csv or .pdf files. A hypothetical dataset was used to illustrate the application of IPDmada.ConclusionsIPDmada will be very helpful to researchers doing DTA IPD‐MA, since it not only facilitates the statistical analysis but also provides a standard framework. The introduction of IPDmada will harmonize the methods used in DTA IPD‐MA and ensure the quality of such analyses.Highlights IPDmada is a newly developed web‐based tool for performing statistical analysis of individual patient data meta‐analysis of diagnostic accuracy and visualizing the results.The tool is freely available to all the researchers, and requiring no installation of statistical software/packages.The tool has an user‐friendly interface, and allows meta‐analysis on both dichotomized and continuous test results. Researchers can easily use this tool to investigate the threshold effect and covariate effect on the summary accuracy.The introduction and implementation of IPDmada will serve as a useful tool for DTA IPD‐MA and increase the quality of such studies.

A comparison of seed germination coefficients using functional regression.

PremiseSeed germination over time is characterized by a sigmoid curve, called a germination curve, in which the percentage (or absolute number) of seeds that have completed germination is plotted against time. A number of individual coefficients have been developed to characterize this germination curve. However, as germination is considered to be a qualitative developmental response of an individual seed that occurs at one time point, but individual seeds within a given treatment respond at different time points, it has proven difficult to develop a single index that satisfactorily incorporates both percentage and rate. The aim of this paper is to develop a new coefficient, the continuous germination index (CGI), which quantifies seed germination as a continuous process, and to compare the CGI with other commonly used indexes.MethodsTo create the new index, the germination curves were smoothed using nondecreasing splines and the CGI was derived as the area under the resulting spline. For the comparison of the CGI with other common indexes, a regression model with functional response was developed.ResultsUsing both an experimentally obtained wild pea (Pisum sativum subsp. elatius) seed data set and a hypothetical data set, we showed that the CGI is able to characterize the germination process better than most other indices. The CGI captures the local behavior of the germination curves particularly well.DiscussionThe CGI can be used advantageously for the characterization of the germination process. Moreover, B‐spline coefficients extracted by its construction can be employed for the further statistical processing of germination curves using functional data analysis methods.

Evaluation of accuracy in the determination of crystal structure factors using large-angle convergent-beam electron diffraction patterns.

The accuracy of electron density distribution analysis using large-angle convergent-beam electron diffraction (LACBED) patterns is evaluated for different convergence angles. An orbital ordered state of FeCr2O4 is used as an example of the analysis. Ideal orbital-ordered and non-ordered states are simulated by using orbital scattering factors. LACBED patterns calculated for the orbital-ordered state were used as hypothetical experimental data sets. Electron density distribution of the Fe 3d orbitals has been successfully reconstructed with a higher accuracy from LACBED patterns with convergence angles larger than 15.2 mrad, which is 4 times as large as that for conventional convergent-beam electron diffraction patterns. Excitation of particular Bloch waves with the aid of LACBED patterns has a key role in the accurate analysis of electron density distributions.

Assessing and Extending Track Quality Index for Novel Measurement Techniques in Railway Systems

A systematic maintenance process is essential to keeping railway systems safe and reliable. However, performing such maintenance is costly and often results in system disruption. There is a tradeoff between system safety and budgetary constraints; understanding the condition of the track infrastructure is essential to find the balance between needs and costs for decisions about when to perform maintenance. In this study, the track quality index (TQI), which is commonly used to evaluate the status of tracks and to decide maintenance interventions, is reviewed, including 12 TQIs for superstructure and six for substructure. A literature review indicates that TQIs for sleepers and subgrade have not yet been developed. The differences between TQIs are compared using a set of hypothetical raw data. Their capabilities for identifying track irregularities are also investigated based on the EN 13848 regulations. To classify TQI characteristics in a systematic way, this study proposes four concepts: accuracy, sensitivity, data required, and specificity. Accuracy indicates a TQI’s capability of detecting defects; sensitivity indicates how TQIs change according to variations in the defects; specificity relates to the amount of parameters considered, and the ability to pinpoint root causes or global consequences of defects. The results suggest a tradeoff between the four concepts, where high sensitivity can increase the ability to detect the smallest defects but may be affected by bias; more parameters considered may indicate low accuracy when detecting a single type of defect. Therefore, this study suggests railway regulators use multiple TQIs with complementary characteristics for classifying track status.

Developing a methodology to predict the adoption rate of Connected Autonomous Trucks in transportation organizations using peer effects

This paper presents a methodology for predicting the adoption rate of Connected Autonomous Trucks (CATs) in transportation organizations using peer effects. There are a number of different factors that must be considered when developing innovation adoption models for organizations. This paper briefly describes each of the relevant variables and combines them into a discrete choice model for predicting the adoption rate of CATs by a hypothetical sample of transportation organizations. The model incorporates new peer effect modeling techniques to simulate the competition and informal communication network. Preliminary results suggest that organizations which are larger are less likely to change their decisions due to the decisions of other, competing organizations, whereas smaller organizations are more easily influenced by the decisions of larger organizations. The methodology developed in this paper produces reasonable results using a hypothetical dataset, and the methodology has been designed to be transferrable to any number of organizational innovations.

Quantifying the Restrictiveness of Theories

We propose a new way to quantify the restrictiveness of an economic model, based on how well the model fits simulated, hypothetical data sets. The data sets are drawn at random from a distribution that satisfies some application-dependent content restrictions (such as that people prefer more money to less). Models that can fit almost all hypothetical data well are not restrictive. To illustrate our approach, we evaluate the restrictiveness of popular behavioral models in two experimental settings|certainty equivalents and initial play| and explain how restrictiveness reveals new insights about each of the models.

EVALUACIÓN DE ÁREAS SUSCEPTIBLES A LA INUNDACIÓN POR MAREA DE TORMENTA GENERADA POR HURACANES EN EL ARCHIPIÉLAGO DE SAN ANDRÉS, PROVIDENCIA Y SANTA CATALINA, COLOMBIA

Debido a la posición geográfica del archipiélago de San Andrés, Providencia y Santa Catalina (SPSC),
 esta es la parte del territorio colombiano más expuesto a ser inundado por marea de tormenta generada por
 ciclones tropicales (CT). Entre los peligros asociados a los CT, las inundaciones suelen ocasionar los mayores
 daños. En este estudio se evalúa un escenario extremo de inundación por marea de tormenta asociada a CT
 para establecer las áreas susceptibles a la inundación en este archipiélago. Dada la escasez de CT históricos
 en este archipiélago, se generó una base de datos de CT hipotéticos, los cuales son un conjunto de eventos
 con una velocidad de viento constante (95.17 m/s), una velocidad de traslación constante de 5.87 m/s y un
 radio máximo de viento constante de 56.3 km, para trayectorias con cinco direcciones de aproximación al
 área de interés. Se evaluaron siete trayectorias paralelas para cada dirección, separadas por 6 km. La misma
 metodología fue utilizada para la isla de San Andrés, y separadamente para Providencia y Santa Catalina, se
 usaron diferentes eventos debido a la distancia entre ellas (90km). El modelo hidrodinámico se forzó con
 campos de viento y presión generados a partir de la base de datos de CT hipotéticos para determinar la marea de
 tormenta e inundación por CT en el archipiélago de SPSC. Los resultados obtenidos incluyen la envolvente de
 las envolventes individuales de la distribución espacial del espejo de agua (nivel del agua referido al nivel
 del terreno) de cada evento, reportando el escenario de inundación más conservador generado por marea
 de tormenta de CT en el archipiélago de SPSC. Las áreas propensas a inundación en la isla de San Andrés
 están ubicadas al Este de la misma, principalmente donde se encuentran los puertos de abrigo y en la parte
 Norte donde se encuentran los principales asentamientos humanos. Para la isla de Providencia estas áreas se
 encuentran al Este, en regiones circunvecinas al aeropuerto y al Norte, a lo largo del canal que la separa de la
 isla de Santa Catalina. Para esta última isla, las áreas propensas a inundación se encuentran al Sureste. Bajo
 este escenario de inundación el porcentaje de área afectada sería de 13.39%, 4.24%, y 4.43% para San Andrés,
 Providencia y Santa Catalina, respectivamente.