Hypothetical Data Research Articles

Unified population register as a source of conducting socio-demographic analysis

The article deals with the analysis of demographic characteristics based on data from unified population register. The role of population registers, which are considered the most important sources of population data, is examined. The possibility of a wider use of the data of the unified population register in socio-demographic research is studied. Demographic characteristics are analyzed in the context of the COVID-19 pandemic based on data from individuals integrated into a single registry for experimental assessment. Experience shows that although data from population registers are currently used in demographic research, there is no approach to using data from other government registers. One of the main features of conducting demographic analysis using data collected in different state registers is the presence of a single identification number for each person. In the study, the information on individuals is presented by hypothetical data taken from two separate registers (population registers and health registers). A dataset on 1,000 individuals was combined into a single register. In this dataset, personal data and information about health of the population were collected. The dataset includes each person's PIN, gender, age, position, number of family members, nationality, chronic diseases including diabetes, bronchial asthma, COVID-19 infection, death from COVID-19, recovery from COVID-19. The obtained results show that the creation of a single integrated register based on the population register — electronic demography system — requires an increase in the number of available statistical indicators, integration of various state registers for a more detailed analysis. This will allow processing data and performing statistical analysis more thoroughly and for a longer period of time.

Investigating the Functional Role of Hypothetical Proteins From an Antarctic Bacterium Pseudomonas sp. Lz4W: Emphasis on Identifying Proteins Involved in Cold Adaptation.

Exploring the molecular mechanisms behind bacterial adaptation to extreme temperatures has potential biotechnological applications. In the present study, Pseudomonas sp. Lz4W, a Gram-negative psychrophilic bacterium adapted to survive in Antarctica, was selected to decipher the molecular mechanism underlying the cold adaptation. Proteome analysis of the isolates grown at 4°C was performed to identify the proteins and pathways that are responsible for the adaptation. However, many proteins from the expressed proteome were found to be hypothetical proteins (HPs), whose function is unknown. Investigating the functional roles of these proteins may provide additional information in the biological understanding of the bacterial cold adaptation. Thus, our study aimed to assign functions to these HPs and understand their role at the molecular level. We used a structured insilico workflow combining different bioinformatics tools and databases for functional annotation. Pseudomonas sp. Lz4W genome (CP017432, version 1) contains 4493 genes and 4412 coding sequences (CDS), of which 743 CDS were annotated as HPs. Of these, from the proteome analysis, 61 HPs were found to be expressed consistently at the protein level. The amino acid sequences of these 61 HPs were submitted to our workflow and we could successfully assign a function to 18 HPs. Most of these proteins were predicted to be involved in biological mechanisms of cold adaptations such as peptidoglycan metabolism, cell wall organization, ATP hydrolysis, outer membrane fluidity, catalysis, and others. This study provided a better understanding of the functional significance of HPs in cold adaptation of Pseudomonas sp. Lz4W. Our approach emphasizes the importance of addressing the “hypothetical protein problem” for a thorough understanding of mechanisms at the cellular level, as well as, provided the assessment of integrating proteomics methods with various annotation and curation approaches to characterize hypothetical or uncharacterized protein data. The MS proteomics data generated from this study has been deposited to the ProteomeXchange through PRIDE with the dataset identifier–PXD029741.

Bayesian sample size determination using commensurate priors to leverage preexperimental data.

This paper develops Bayesian sample size formulae for experiments comparing two groups, where relevant preexperimental information from multiple sources can be incorporated in a robust prior to support both the design and analysis. We use commensurate predictive priors for borrowing of information and further place Gamma mixture priors on the precisions to account for preliminary belief about the pairwise (in)commensurability between parameters that underpin the historical and new experiments. Averaged over the probability space of the new experimental data, appropriate sample sizes are found according to criteria that control certain aspects of the posterior distribution, such as the coverage probability or length of a defined density region. Our Bayesian methodology can be applied to circumstances that compare two normal means, proportions, or event times. When nuisance parameters (such as variance) in the new experiment are unknown, a prior distribution can further be specified based on preexperimental data. Exact solutions are available based on most of the criteria considered for Bayesian sample size determination, while a search procedure is described in cases for which there are no closed-form expressions. We illustrate the application of our sample size formulae in the design of clinical trials, where pretrial information is available to be leveraged. Hypothetical data examples, motivated by a rare-disease trial with an elicited expert prior opinion, and a comprehensive performance evaluation of the proposed methodology are presented.

Bayesian sample size determination using commensurate priors to leverage pre-experimental data.

This paper develops Bayesian sample size formulae for experiments comparing two groups, where relevant pre-experimental information from multiple sources can be incorporated in a robust prior to support both the design and analysis. We use commensurate predictive priors for borrowing of information, and further place Gamma mixture priors on the precisions to account for preliminary belief about the pairwise (in)commensurability between parameters that underpin the historical and new experiments. Averaged over the probability space of the new experimental data, appropriate sample sizes are found according to criteria that control certain aspects of the posterior distribution, such as the coverage probability or length of a defined density region. Our Bayesian methodology can be applied to circumstances that compare two normal means, proportions or event times. When nuisance parameters (such as variance) in the new experiment are unknown, a prior distribution can further be specified based on pre-experimental data. Exact solutions are available based on most of the criteria considered for Bayesian sample size determination, while a search procedure is described in cases for which there are no closed-form expressions. We illustrate the application of our sample size formulae in the design of clinical trials, where pre-trial information is available to be leveraged. Hypothetical data examples, motivated by a rare-disease trial with elicited expert prior opinion, and a comprehensive performance evaluation of the proposed methodology are presented.

Anchors on prices of consumer goods only hold when decisions are hypothetical.

We elicit willingness to pay for different types of consumption goods, systematically manipulating irrelevant anchors (high vs. low) and incentives to provide true valuations (hypothetical questions vs. Becker-DeGroot-Marschak mechanism). On top of a strong hypothetical bias, we find that anchors only make a substantial, significant difference in the case of hypothetical data, the first experiments to directly document such an interaction. This finding suggests that hypothetical market research methods may deliver lower quality data. Moreover, it contributes to the discussion examining the mechanism underlying the anchoring effect, suggesting it could partly be caused by insufficient conscious effort to drift away from the anchor.

Определение при пескопроявлении оптимального режима эксплуатации нефтяной скважины, продуцирующей неньютоновскую нефть

The article proposes a method for determining the technological regime of a well, in which it is operated without accumulation of sand in the wellbore, which is carried out of the reservoir during filtration in the reservoir of non-Newtonian oil. On the basis of the proposed method, with hypothetical data, a numerical calculation was carried out and a specific calculation example was given.

Estimation of groundwater variations in an anisotropic leaky aquifer: A ditch drain model

This paper deals with the development of mathematical models for water table fluctuation in 2-D anisotropic aquifer. Time dependent recharge and withdrawal are taken into consideration. A Boussineq equation - nonlinear partial differential equation governs the flow. The PDE is solved using finite Fourier sine transforms and closed from expression are obtained. The sensitivity of the parameters is tested using hypothetical data. Effect of leaky nature of aquifer base on the water table has been analyzed.

How to determine best diversity ordering method for a community data set?

Background: We conducted a study about how to determine best diversity ordering method for a community data set. Using 12 hypothetical and one ecological datasets, we tested the performances of 20 diversity ordering ( divo) methods based on four criteria. Number of intersections ( ints) amongst the diversity curves was taken the most important criterion into account. We defined the other criteria considering whether parametric values of a divo method contains SHD (species richness, Shannon entropy and Simpson index), potentially uSHD (unbiased values of SHD), and potentially true species diversity, tSHD (bias corrected values as effective number of SHD). All the criteria were collected into an equation called the relative selection value, \begin{equation}{rVd}_i \end{equation}. Results: According to the \begin{equation}{rVd}_i \end{equation} values of hypothetical community data sets, the best performances in seven community data sets were provided by \begin{equation}N_\alpha \end{equation}. This was followed by intrinsic diversity related methods with five community data sets. For ecological data set, the best results were obtained from the methods, \begin{equation}\left(i,\ M_i\right) \end{equation}, \begin{equation}\left(\log{i},\ k_i\right) \end{equation} and \begin{equation}N_\alpha \end{equation}, with the \begin{equation}{rVd}_i \end{equation} values of 6.883, 6.881 and 6.859, respectively. Conclusion: Findings suggest that the characteristics of community data sets play important role in defining the best diversity ordering method. This tells us that diversity is certainly a multifaceted phenomenon for a single community but perhaps it is a single phenomenon for a group of communities.

Reconstructing Gene Gains and Losses with BadiRate.

Estimating gene gain and losses is paramount to understand the molecular mechanisms underlying adaptive evolution. Despite the advent of high-throughput sequencing, such analyses have been so far hampered by the poor contiguity of genome assemblies. The increasing affordability of long-read sequencing technologies will however revolutionize our capacity to identify gene gains and losses at an unprecedented resolution, even in non-model organisms. To thoroughly exploit all such multigene family variation, the software BadiRate implements a collection of birth-and-death stochastic models, aiming at estimating by maximum likelihood the gene turnover rates along the internal and external branches of a given phylogenetic species tree. Its statistical framework also provides versatility for inferring the gene family content at the internal phylogenetic nodes (and to estimate the minimum number of gene gains and losses in each branch), for statistically contrasting competing hypotheses (e.g., accelerations of the gene turnover rates at pre-defined clades), and for pinpointing gene family expansions or contractions likely driven by natural selection. In this chapter we review the theoretical models implemented in BadiRate and illustrate their applicability by analyzing a hypothetical data set of 14 microbial species.

Process simulation of modelled reverse osmosis for desalination of seawater

Abstract Model equations for prediction of process parameters of reverse osmosis for desalination of seawater were developed via mathematical derivation from basic equations for the reverse osmosis process. A model equation relating the interfacial solute concentration () with the process pressure difference () was developed. Taking the of reverse osmosis as the basic independent variable, further model equations relating other process parameters such as the solute concentration polarity , water flux , osmotic pressure , water output rate (q), power density (Pd) and specific energy consumption (SEC) were developed. Simulation of hypothetical reverse osmosis data using Microsoft Excel Worksheet and Microsoft Windows 10 on a 64-bit operating system was carried out. Simulation results showed that the optimum fluid bulk concentration was = 0.0004 mole/cm3. The optimum rate of increase in the solute rejection factor per unit rise in ΔP was 0.45%. The optimum solute rejection factor was 97.6%. The optimum water output rate, specific energy consumption and power density were 103.2 L/h, 3.65 kWh/m3 and 6.09 W/m2, respectively.

Data entropy decomposition into functional and uncertainty components

Information entropy is a fundamental property of measurement data and can be decomposed into functional and relative uncertainty components. The purpose of this paper is to mathematically isolate functional entropy and to demonstrate its application to model data. The equation for functional entropy is developed and its utility is demonstrated using hypothetical data that describes different processes that contribute to the same effect. Functional entropy assigns an effectiveness value to these processes in the fundamental units of information, which makes it a useful tool for comparing different data sets and their associated processes.

Bertrand competition under asymmetric conditions

Abstract Analyzing price competition through game theory is one of the most important frameworks of oligopoly theory, especially in industrial organizations. Numerous studies have been conducted in this direction, as companies are forced to adjust their sustainable pricing policy to operate in the long term. Thus, the players make a regular adjustment of the pricing strategy. Of all the models developed based on Bertrand’s reference model (1883), the most analyzed were those in which informational symmetry predominated. Since informational symmetry presents only a theoretical framework, economists have turned their attention to information asymmetry. This type of information best describes a complex economic game, as it creates an information gap between players and generates opportunities in the decision-making process. Thus, asymmetric information is the main parameter of the decision-making process that determines decision makers to resort to different decision strategies than those assumed by the analytical model. Any asymmetric Bertrand model predicts competitive and efficient market outcomes, a direct relationship between a firm’s conduct, market structure and finally market performance. This paper undertakes an analysis of the Bertrand model in the case of demand and asymmetric costs. We determine the Bertrand-Nash equilibrium under the scenario in which close, but not perfect substitutes exist for the differentiated product with hypothetical data. Then we highlight what happens with profits when we consider that discrete cross marginal demand gradually increases. For more research, there are numerous studies based on sequential games, Bayesian games and signaling games both in discrete time and in continuous time.

Interpreting a standardized and normalized measure of neighborhood socioeconomic status for a better understanding of health differences

BackgroundStandardization and normalization of continuous covariates are used to ease the interpretation of regression coefficients. Although these scaling techniques serve different purposes, they are sometimes used interchangeably or confused for one another. Therefore, the objective of this study is to demonstrate how these scaling techniques lead to different interpretations of the regression coefficient in multilevel logistic regression analyses.MethodsArea-based socioeconomic data at the census tract level were obtained from the 2015–2019 American Community Survey for creating two measures of neighborhood socioeconomic status (SES), and a hypothetical data on health condition (favorable versus unfavorable) was constructed to represent 3000 individuals living across 300 census tracts (i.e., neighborhoods). Two measures of neighborhood SES were standardized by subtracting its mean and dividing by its standard deviation (SD) or by dividing by its interquartile range (IQR), and were normalized into a range between 0 and 1. Then, four separate multilevel logistic regression analyses were conducted to assess the association between neighborhood SES and health condition.ResultsBased on standardized measures, the odds of having unfavorable health condition was roughly 1.34 times higher for a one-SD change or a one-IQR change in neighborhood SES; these reflect a health difference of individuals living in relatively high SES (relatively affluent) neighborhoods and those living in relatively low SES (relatively deprived) neighborhoods. On the other hand, when these standardized measures were replaced by its respective normalized measures, the odds of having unfavorable health condition was roughly 3.48 times higher for a full unit change in neighborhood SES; these reflect a health difference of individuals living in highest SES (most affluent) neighborhoods and those living in lowest SES (most deprived) neighborhoods.ConclusionMultilevel logistic regression analyses using standardized and normalized measures of neighborhood SES lead to different interpretations of the effect of neighborhood SES on health. Since both measures are valuable in their own right, interpreting a standardized and normalized measure of neighborhood SES will allow us to gain a more rounded view of the health differences of individuals along the gradient of neighborhood SES in a certain geographic location as well as across different geographic locations.

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.

What do (some of) our association measures measure (most)? Association?

AbstractThis paper discusses the degree to which some of the most widely-used measures of association in corpus linguistics are not particularly valid in the sense of actually measuring association rather than some amalgam of a lot of frequency and a little association. The paper demonstrates these issues on the basis of hypothetical and actual corpus data and outlines implications of the findings. I then outline how to design an association measure that only measures association and show that its behavior supports the use of the log odds ratio as a true association-only measure but separately from frequency; in addition, this paper sets the stage for an analogous review of dispersion measures in corpus linguistics.

Statistical Analysis of Determinants of Institutional Delivery Service among Reproductive Age Women in Ethiopia

Health care services during pregnancy and during delivery are important for the survival and wellbeing of both the mother and the infant so that increasing institutional deliveries is a basic concern for reducing maternal and neonatal mortality. This article was focused on assessing the trend of institutional delivery service and identifying associated factors using basic statistical tools. The analysis was based on the hypothetical data of 5753 women in reproductive age carefully managed from Ethiopian Mini Demographic and Health Survey (EMDHS) 2019. The trend of institutional delivery revealed a sharp increase from 26% in 2016 to 49.92% in 2019 based on DHS data of the respective years. The study identified variables such as region, place of residence, education level, sex of household head, wealth index, number of living children and antenatal care as highly significant determinants of institutional delivery service in Ethiopia. Institutional delivery service increases with better access to education, living in urban, being in better off wealth category, and following antenatal care visit. The study recommended that there should be well equipped health facilities for pregnant women at each stage.

Data assimilation and agent-based modelling: towards the incorporation of categorical agent parameters

This paper explores the use of a particle filter—a data assimilation method—to incorporate real-time data into an agent-based model. We apply the method to a simulation of real pedestrians moving through the concourse of Grand Central Terminal in New York City (USA). The results show that the particle filter does not perform well due to (i) the unpredictable behaviour of some pedestrians and (ii) because the filter does not optimise the categorical agent parameters that are characteristic of this type of model. This problem only arises because the experiments use real-world pedestrian movement data, rather than simulated, hypothetical data, as is more common. We point to a potential solution that involves resampling some of the variables in a particle, such as the locations of the agents in space, but keeps other variables such as the agents’ choice of destination. This research illustrates the importance of including real-world data and provides a proof of concept for the application of an improved particle filter to an agent-based model. The obstacles and solutions discussed have important implications for future work that is focused on building large-scale real-time agent-based models.

Fear effect in discrete prey-predator model incorporating square root functional response

In this work, an interaction between prey and its predator involving the effect of fear in presence of the predator and the square root functional response is investigated. Fixed points and their stability condition are calculated. The conditions for the occurrence of some phenomena namely Neimark-Sacker, Flip, and Fold bifurcations are given. Base on some hypothetical data, the numerical simulations consist of phase portraits and bifurcation diagrams are demonstrated to picturise the dynamical behavior. It is also shown numerically that rich dynamics are obtained by the discrete model as the effect of fear.

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.

OPTIMIZATION ALGORITHM OF RESOURCE ALLOCATION IN ORDER TO EXAMINE THE ECONOMIC EFFECTS OF LINEAR PROGRAMMING MODEL RESPONSE

The concept of examining decisions using abstract research constructions as virtual aids enables the design of the management platform and the creation of the behavior of the future state of the system. Checking the effects of decisions in a real system through an appropriate mathematical-logical apparatus certainly affects the reduction of randomness and spontaneity of management. The symbolic connection of the input that produces the combined input action and the output that manifests the reaction expressed by the degree of sensitivity and enables the translation of the imaginary expression of reality into the operative mathematical language. The commitment to this type of model is based on the fact of almost standardized procedures in terms of choosing the priority of including certain forms of resources, which makes it representative in terms of achieving the goal function defined by the criterion of optimality. In this sense, we can talk about a whole range of optimization problems with mutually inconsistent tactical goals and thus the need for their refined balance, and one of such challenges is the focus of attention of a specific research project. The mathematical model represents a formalized description of the action of physical contents, and we use it to support the decision-making process when the complexity of reality allows that. Accordingly, to express the regularity of functioning, a deterministic model of linear programming in the form of a set of relations describing the input-output effect of influential factors used in this paper. With a tolerant simplification of reality, the perception of the model, based on the coincidence with the structure of the original, actually shows an approximation that means analogous behavior demonstrated on the quantitative model or its derivative - the optimization model. This thesis supports the fact that the corresponding relationship between the model and the original allows the examination of the behavior of the observed hypothetical system, and the analysis of the sensitivity map of the variable, in the form of a goal function, becomes a source of modeling information. Also, the system is not influenced by a high rate of change that would mean expanding from deterministic to stochastic modeling, but all methodological resources are mobilized so that the model concept would not become a theoretical misconception that would lead to quasi-decision making. The author’s analytical thinking and logic try to give a chance to the imagination, which connects creativity and physical resources that indicates several alternatives, which contain future parameters. In this way, the authors use the model as an increment of knowledge to connect the a priori management reserve of their accuracy with the reality of hypothetical data within a rational approach.