Empirical Fit To Data Research Articles

Statistical physics of azo reactive dye adsorption by metal hydroxide sludge for water remediation

We performed a theoretical investigation of azo reactive red (RR-120) on metal hydroxide sludge (MHS) using five advanced statistical physics models to interpret thermodynamic equilibrium data for potential water remediation applications. Each model incorporates multiple physicochemical parameters to comprehensively describe the microscopic process through empirical data fitting. The thermodynamic functions (entropy, internal energy and Gibbs free energy) were analyzed using the grand canonical formalism. Our analysis indicated that the single-energy monolayer model represents the most realistic scenario. The adhesion of RR-120 onto MHS was endothermic with an adsorption energy below 40.0 kJ/mol suggesting that Van der Waals interactions and hydrogen bonding are the predominant driving forces. The stereographic analysis demonstrated that as the density of receptor sites (Nm) incremented, there was a corresponding decline in the number of anchored entities per site (n). Moreover, thermodynamic assessment revealed that the disorder increased before half-saturation and decreased after half-saturation while negativity of Gibbs free energy indicated a spontaneous process of RR-120 adsorption in all tested temperatures. The exceptional adsorption capabilities of MHS will open up new avenues in wastewater treatment, soil remediation and air purification.

Аппроксимация статистических данных заболеваемости коронавирусной инфекцией с учетом расслоения по сопутствующим диагнозам

The article considers the stratification of concomitant diagnoses of Covid-19 recovery statistics for the city of Irkutsk for 2020-2021. The previous study was conducted without taking into account such stratification. Various options for approximating real statistics by Gaussian and Lorentz functions, gamma distribution, and Johnson curves are considered. It is shown that the stratification of recovery statistics improves the approximation of Gaussian and Lorentz functions in comparison with integral statistics, and the construction of an approximation based on the Lorentz function always describes the real statistics better. Estimates of mathematical expectation and variance based on statistical data are consistent with estimates of these values based on the Gaussian approximation of statistics by the least squares method, i.e. the approaches are equivalent. At the same time, calculations of the Pearson Chi-squared criterion reject the hypothesis that empirical data correspond to the assumed theoretical distribution. Therefore, we cannot talk about finding the distribution function, but only about approximating statistics by certain types of curves. The fitting of empirical data by Gaussian and Lorentz functions was carried out using the least squares method. In general, the approximation error due to the stratification of statistics on concomitant diagnoses decreases from 6% to 3%.

A full‐probabilistic cloud analysis for structural seismic fragility via decoupled M‐PDEM

AbstractPerformance‐based earthquake engineering (PBEE) is essential for ensuring engineering safety. Conducting seismic fragility analysis within this framework is imperative. Existing methods for seismic fragility analysis often rely heavily on double loop reanalysis and empirical data fitting, leading to challenges in obtaining high‐precision results with a limited number of representative structural analysis instances. In this context, a new methodology for seismic fragility based on a full‐probabilistic cloud analysis is proposed via the decoupled multi‐probability density evolution method (M‐PDEM). In the proposed method, the assumption of a log‐normal distribution is not required. According to the random event description of the principle of preservation of probability, the transient probability density functions (PDFs) of intensity measure (IM) and engineering demand parameter (EDP), as key response quantities of the seismic‐structural system, are governed by one‐dimensional Li‐Chen equations, where the physics‐driven forces are determined by representative analysis data of the stochastic dynamic system. By generating ground motions based on representative points of basic random variables and performing structural dynamic analysis, the decoupled M‐PDEM is employed to solve the one‐dimensional Li‐Chen equations. This yields the joint PDF of IM and EDP, as well as the conditional PDF of EDP given IM, resulting in seismic fragility analysis outcomes. The numerical implementation procedure is elaborated in detail, and validation is performed using a six‐story nonlinear reinforced concrete (RC) frame subjected to non‐stationary stochastic ground motions. Comparative analysis against Monte Carlo simulation (MCS) and traditional cloud analysis based on least squares regression (LSR) reveals that the proposed method achieves higher computational precision at comparable structural analysis costs. By directly solving the physics‐driven Li‐Chen equations, the method provides the full‐probabilistic joint information of IM and EDP required for cloud analysis, surpassing the accuracy achieved by traditional methods based on statistical moment fitting and empirical distribution assumptions.

A General Evaluation System for Optimal Selection Performance of Radar Clutter Model

The optimal selection of radar clutter model is the premise of target detection, tracking, recognition, and cognitive waveform design in clutter background. Clutter characterization models are usually derived by mathematical simplification or empirical data fitting. However, the lack of standard model labels is a challenge in the optimal selection process. To solve this problem, a general three-level evaluation system for the model selection performance is proposed, including model selection accuracy index based on simulation data, fit goodness indexs based on the optimally selected model, and evaluation index based on the supporting performance to its third-party. The three-level evaluation system can more comprehensively and accurately describe the selection performance of the radar clutter model in different ways, and can be popularized and applied to the evaluation of other similar characterization model selection.

Investigation of Zebrafish Embryo Membranes at Epiboly Stage through Electrorotation Technique.

A preliminary exploration of the physiology and morphology of the zebrafish embryo (ZFE) during the late-blastula and early-gastrula stages through its electrical properties was performed, applying the electrorotation (ROT) technique. This method, based on induced polarizability at the interfaces, was combined with an analytical spherical shell model to obtain the best fit of empirical data and the desired information, providing a means of understanding the role of different membranes. Suspended in two solutions of low conductivity, the major compartments of the ZFE were electrically characterized, considering morphological data from both observed records and data from the literature. Membrane integrity was also analyzed for dead embryos. The low permeability and relatively high permittivity obtained for the chorion probably reflected both its structural characteristics and external conditions. Reasonable values were derived for perivitelline fluid according to the influx of water that occurs after the fertilization of the oocyte. The so-called yolk membrane, which comprises three different and contiguous layers at the epiboly stage, showed atypical electrical values of the membrane, as did the yolk core with a relatively low permittivity. The internal morphological complexity of the embryo itself could be addressed in future studies by developing an accurate geometric model.

Network-augmented compartmental models to track asymptomatic disease spread.

A major challenge in understanding the spread of certain newly emerging viruses is the presence of asymptomatic cases. Their prevalence is hard to measure in the absence of testing tools, and yet the information is critical for tracking disease spread and shaping public health policies. Here, we introduce a framework that combines classic compartmental models with travel networks and we use it to estimate asymptomatic rates. Our platform, traSIR ("tracer"), is an augmented susceptible-infectious-recovered (SIR) model that incorporates multiple locations and the flow of people between them; it has a compartment model for each location and estimates of commuting traffic between compartments. TraSIR models both asymptomatic and symptomatic infections, as well as the dampening effect symptomatic infections have on traffic between locations. We derive analytical formulae to express the asymptomatic rate as a function of other key model parameters. Next, we use simulations to show that empirical data fitting yields excellent agreement with actual asymptomatic rates using only information about the number of symptomatic infections over time and compartments. Finally, we apply our model to COVID-19 data consisting of reported daily infections in the New York metropolitan area and estimate asymptomatic rates of COVID-19 to be ∼34%, which is within the 30-40% interval derived from widespread testing. Overall, our work demonstrates that traSIR is a powerful approach to express viral propagation dynamics over geographical networks and estimate key parameters relevant to virus transmission. No public repository.

Experimental Performance Analysis of Hardware-Based Link Quality Estimation Modelling Applied to Smart Grid Communications

The smart grid is the modern electricity grid, which significantly improves the efficiency, reliability, and sustainability of electricity transmission systems. The advanced metering infrastructure (AMI) system, which is the essential system in the smart grid, enables real-time data collection and data analysis obtained from smart meters (SMs) and other devices through last-mile communication networks. In this paper, the hardware-based link quality estimation (LQE) was modeled, namely an SNR-based model, a mapping model, and an RSSI- and PRR-based logistic regression model, and their performance was then evaluated by the root mean-squared error (RMSE) with the empirical data. The SNR-based and mapping models were formulated by the packet error probability, whereas the RSSI- and PRR-based logistic regression model was formulated by the empirical data fitting. The RSSI- and PRR-based logistic regression model outperformed the other two models, with an RMSE difference of 111–122%. These LQE models can be implemented on SMs or modems to monitor the reliability and efficiency of the AMI last-mile communication network.

The Impact of Live Commerce and Short Video on College Students Actual Purchase

The emergence of new media has led to live commerce becoming a novel, widely adopted trend, and live commerce has also become a new business model. This study has put a lot of work into creating an integrated model that combines the theories of planned behavior and media exposure to explain the connection between watching short videos and in-person shopping. We looked at the connections between attitude, subjective norm, perceived behavioral control, frequency, duration, and attention to live commerce viewing, as well as real purchases. To check the accuracy of the fictitious model, 402 college students in China participated in a questionnaire study that yielded empirical data. The structural equation model is shown to have a strong model fit for empirical data, while the measurement model demonstrates that the theoretical constructs have appropriate reliability and validity. The results of the study demonstrate that customers attitudes toward live commerce and their perceived behavioral control over it greatly influenced their actual purchases. But the effect of subjective norms on actual purchases has not been verified. This research addressed the knowledge gap in the application of TPB to explain offline and online purchasing behavior. Also, this study has its limitation; since the research is primarily focused on China, it is difficult to extrapolate the findings to other countries.

Mixed-mode dynamic fracture parameters for soda-lime glass

Dynamic mixed-mode fracture of soda-lime glass (SLG) was experimentally investigated using a full-field optical method - Digital Gradient Sensing (DGS) - in conjunction with ultrahigh-speed photography. Single edge-notched specimens were subjected to reverse impact loading using a modified Hopkinson pressure bar. The specimens were eccentrically loaded at different offset distances relative to the initial notch to achieve a wide range of mode-mixities, from mode-I to nearly mode-II condition, at initiation. Two time-resolved orthogonal angular deflection fields of light rays proportional to the respective full-field stress gradients were optically measured during experiments on different geometries. Mode-I and -II stress intensity factor histories spanning pre- and post-initiation behaviors were evaluated via over-deterministic least-squares error minimization of optically measured full-field data. By considering the critical stress intensity factors at different crack initiation modes, a fracture envelope for SLG encompassing various mode-mixities was developed for the first time for SLG and compared with predictions from prevailing approaches. An empirical fit of data seems to capture the overall trend whereas the prevailing methods do not. Measured crack kink angles are compared with the popular MTS and SED fracture criteria; both predict the kink angles reasonably well. Unlike brittle polymers, the critical effective stress intensity factors for SLG are independent of mode-mixity over a large range of values but show a decreasing trend as mode-II conditions become dominant and an extrapolated critical mode-II stress intensity factor of 0.37 MPa√m, approx. one-half of its mode-I counterpart, is seen.

Validating knowledge creation factors for community resilience to disaster using structural equation modelling

Knowledge has an important role in reducing disaster risk. However, at the level of policy and practice, this issue is rarely dealt with in a systematic and in-depth manner. Therefore, using an approach based on the Dynamic Theory of Organisational Knowledge Creation of Nonaka (1994), this research intends to develop and analyse the structure of a new Knowledge Creation model to increase community resilience. This study used a cross-sectional design survey of 537 respondents who live in coastal cities in Indonesia. Data analysis was conducted using the Structural Equation Modelling (SEM) method. The results of the model feasibility test show that the hypothesised pathway among the variables under study has a strong statistical significance. The variables are: i) knowledge assets; ii) ba; iii) Socialisation, Externalisation, Combination, and Internalisation (SECI); iv) enablers; v) disaster preparedness; and vi) community resilience. Therefore, this study concludes that the proposed Knowledge Creation for Community Resilience (KCCR) model provides a fairly good fit for empirical data. It is hoped that the KCCR model that has been developed in this study will be of use to education authorities and disaster management agencies, at both local and national levels, and particularly in influencing the planning and implementation of community preparedness in facing disasters. This research is expected to enrich studies in the field of knowledge management, especially in the public knowledge domain, in dealing with disasters and providing recommendations, for policymakers and practitioners to incorporate aspects of knowledge management into efforts to increase community resilience.

A parallelization model for performance characterization of Spark Big Data jobs on Hadoop clusters

This article proposes a new parallel performance model for different workloads of Spark Big Data applications running on Hadoop clusters. The proposed model can predict the runtime for generic workloads as a function of the number of executors, without necessarily knowing how the algorithms were implemented. For a certain problem size, it is shown that a model based on serial boundaries for a 2D arrangement of executors can fit the empirical data for various workloads. The empirical data was obtained from a real Hadoop cluster, using Spark and HiBench. The workloads used in this work were included WordCount, SVM, Kmeans, PageRank and Graph (Nweight). A particular runtime pattern emerged when adding more executors to run a job. For some workloads, the runtime was longer with more executors added. This phenomenon is predicted with the new model of parallelisation. The resulting equation from the model explains certain performance patterns that do not fit Amdahl’s law predictions, nor Gustafson’s equation. The results show that the proposed model achieved the best fit with all workloads and most of the data sizes, using the R-squared metric for the accuracy of the fitting of empirical data. The proposed model has advantages over machine learning models due to its simplicity, requiring a smaller number of experiments to fit the data. This is very useful to practitioners in the area of Big Data because they can predict runtime of specific applications by analysing the logs. In this work, the model is limited to changes in the number of executors for a fixed problem size.

An Empirical Study on the Factors Affecting Elderly Users' Continuance Intention of Shared Nurses.

ObjectiveBased on the technical acceptance model, expectation confirmation model, and perceived risk theory, this study aims to analyze the factors and their effects on shared nurse users’ continuance intention in the process of e-health consumption.MethodsThis research established a measurement tool that fits the topic of this study and a model of shared nurse continuance. From January to May 2020, 373 valid samples from elderly individuals living in urban areas of Jiangxi Province, China, were collected by convenience sampling in order to analyze through empirical research their continuance intentions of selecting shared nurses. The theoretical models and research hypotheses were verified by structural equation modeling with AMOS 25 software.ResultsThe measurement model indicated that the theoretical constructs have adequate reliability and validity, while the structured equation model is illustrated as having a high model fit for empirical data. The hypothesis test results showed that expectation confirmation positively affects perceived ease of use; both of them have positive effects on perceived usefulness and satisfaction. Perceived usefulness and satisfaction play an intermediary role in expectation confirmation and continuance intention. Perceived ease of use and perceived usefulness positively affect continuance intention. Perceived risk negatively affects perceived usefulness and continuance intention.ConclusionThis study expanded the application of the technology acceptance model, expectation confirmation model, and perceived risk model in e-health by investigating the factors that influence elderly users’ continued intention to use shared nurses. Based on these empirical findings, we derived implications for the design and operation of the shared nurse platform, and suggestions on relevant management departments and incentive structures for using e-health. The results of this study provide important implications for further research and practice of mobile health care.

Visualizing a neural network that develops quantum perturbation theory

In this letter, motivated by the question that whether the empirical fitting of data by neural network can yield the same structure of physical laws, we apply the neural network to a simple quantum mechanical two-body scattering problem with short-range potentials, which by itself also plays an important role in many branches of physics. We train a neural network to accurately predict $ s $-wave scattering length, which governs the low-energy scattering physics, directly from the scattering potential without solving Schr\"odinger equation or obtaining the wavefunction. After analyzing the neural network, it is shown that the neural network develops perturbation theory order by order when the potential increases. This provides an important benchmark to the machine-assisted physics research or even automated machine learning physics laws.

Wstępna charakterystyka psychometryczna polskiej wersji kwestionariusza Religious Commitment Inventory-10 (RCI-10-PL) Everetta Worthingtona i współpracowników

The article presents the theoretical background and the psychometric properties of the Polish version of the Religious Commitment Inventory-10 (RCI-10-PL). Confirmatory factor analysis (CFA) was conducted on two samples: 273 participants (Sample A) and 308 participants (Sample B). The results of the CFA showed an acceptable fit of empirical data to the one-factor and two-factor theoretical models, the latter model having a slightly better fit. Moreover, we obtained satisfactory values of the reliability coefficient. Cronbach alpha coefficients for the Polish version of the RCI scales ranged from .82 to .95. Test-retest reliability correlations ranged from .90 to .91 for a five- -week interval between ratings. We also found positive correlations between specific dimensions of RCI-10-PL and religious attitudes, ethical sensibility, and focus on the achievement of harmony in life, which confirms the external validity of the instrument. The results of the analysis conducted on Polish samples are similar to the results obtained from American samples.

Wstępna charakterystyka psychometryczna polskiej wersji kwestionariusza Religious Commitment Inventory-10 (RCI-10-PL) Everetta Worthingtona i współpracowników

The article presents the theoretical background and the psychometric properties of the Polish version of the Religious Commitment Inventory-10 (RCI-10-PL). Confirmatory factor analysis (CFA) was conducted on two samples: 273 participants (Sample A) and 308 participants (Sample B). The results of the CFA showed an acceptable fit of empirical data to the one-factor and two-factor theoretical models, the latter model having a slightly better fit. Moreover, we obtained satisfactory values of the reliability coefficient. Cronbach alpha coefficients for the Polish version of the RCI scales ranged from .82 to .95. Test-retest reliability correlations ranged from .90 to .91 for a five- -week interval between ratings. We also found positive correlations between specific dimensions of RCI-10-PL and religious attitudes, ethical sensibility, and focus on the achievement of harmony in life, which confirms the external validity of the instrument. The results of the analysis conducted on Polish samples are similar to the results obtained from American samples.

Physicochemical effect of activation temperature on the sorption properties of pine shell activated carbon.

Activated carbons produced from a variety of raw materials are normally selective towards a narrow range of pollutants present in wastewater. This study focuses on shifting the selectivity of activated carbon from inorganic to organic pollutants using activation temperature as a variable. The material produced from carbonization of pine shells substrate was activated at 250°C and 850°C. Both adsorbents were compared with commercial activated carbon for the sorption of lead, cadmium, methylene blue, methyl blue, xylenol orange, and crystal violet. It was observed that carbon activated at 250°C was selective for lead and cadmium whereas the one activated at 850°C was selective for the organic dyes. The Fourier transform infrared spectroscopy study revealed that AC850 had less surface functional groups as compared to AC250. Point of zero charge and point of zero salt effect showed that AC250 had acidic groups at its surface. Scanning electron microscopy depicted that increase in activation temperature resulted in an increase in pore size of activated carbon. Both AC250 and AC850 followed pseudo-second-order kinetics. Temkin isotherm model was a best fit for empirical data obtained at equilibrium. The model also showed that sorption process for both AC250 and AC850 was physisorption.

Pro-environmental Purchasing Behavior of Consumers

This study aims to examine the value of personal norms in addition to the theory of planned behavior (TPB) variables (i.e., attitude toward behavior, subjective norm, perceived behavioral control, and behavioral intention) in explaining consumers’ pro-environmental purchasing behavior. The hypotheses and model were formulated and tested with structural equation modeling using the data from 281 consumers who are active members of a U.S.-based recycling company. Model fit statistics indicate a good fit of empirical data and model structure for pro-environmental purchasing behavior. The findings suggest that while personal and subjective norms, attitudes toward behavior, and intention explain consumers’ pro-environmental purchasing behavior, perceived behavioral control does not have any power in explaining behavior-related intention. Policy makers and marketing professionals are advised to adopt various social and sustainability marketing strategies that focus on communicating different normative aspects of purchasing decisions to promote pro-environmental consumer behaviors. The normative concerns covered in the environmental behavior studies are mostly limited to “subjective norms” as represented in the TPB, which has been widely adopted in the behavioral studies. By extending the TPB with “personal norms,” this study contributes to the better explanation of environmentally relevant purchase behaviors of consumers.

Tests of species-specific models reveal the importance of drought in postglacial range shifts of a Mediterranean-climate tree: insights from integrative distributional, demographic and coalescent modelling and ABC model selection.

Past climate change has caused shifts in species distributions and undoubtedly impacted patterns of genetic variation, but the biological processes mediating responses to climate change, and their genetic signatures, are often poorly understood. We test six species-specific biologically informed hypotheses about such processes in canyon live oak (Quercus chrysolepis) from the California Floristic Province. These hypotheses encompass the potential roles of climatic niche, niche multidimensionality, physiological trade-offs in functional traits, and local-scale factors (microsites and local adaptation within ecoregions) in structuring genetic variation. Specifically, we use ecological niche models (ENMs) to construct temporally dynamic landscapes where the processes invoked by each hypothesis are reflected by differences in local habitat suitabilities. These landscapes are used to simulate expected patterns of genetic variation under each model and evaluate the fit of empirical data from 13 microsatellite loci genotyped in 226 individuals from across the species range. Using approximate Bayesian computation (ABC), we obtain very strong support for two statistically indistinguishable models: a trade-off model in which growth rate and drought tolerance drive habitat suitability and genetic structure, and a model based on the climatic niche estimated from a generic ENM, in which the variables found to make the most important contribution to the ENM have strong conceptual links to drought stress. The two most probable models for explaining the patterns of genetic variation thus share a common component, highlighting the potential importance of seasonal drought in driving historical range shifts in a temperate tree from a Mediterranean climate where summer drought is common.

English

The aim of this study was to study the components of the internal control-based administrative system in secondary schools, and make a Confirmatory Factor Analysis (CFA) to confirm the goodness of fit of empirical data and component model that resulted from the CFA. The study consisted of three steps: 1) studying of principles, ideas, and theories in related documents, 2) synthesizing and specifying them as components of the system, and 3) proposing these components to experts in the field for their confirmation on the appropriateness as components. Sample groups for the CFA comprised three groups with 300 samples i.e. 100 school directors, 100 school deputy directors, and 100 school teachers. A package program was applied to analyze collected data for mean, and standard deviation. Evaluation of the goodness of fit of the CFA Model was done by Chi-square, CFI, TLI, RMSEA and SRMR. Research results revealed that there were four components of the system i.e. Inputs, Process, Outputs, and Feedback. The Inputs comprised four components: 1) administrative resources, 2) learners, 3) administrative tasks in schools, and 4) policies, ideas, and directions on school administration. The Process comprised five components: 1) control environment including seven sub-components, 2) risk assessment including five sub-components, 3) control activities including seven sub- components, 4) information and communication including six sub-components, and 5) monitoring including three sub-components. The Outputs were considered from two results i.e. 1) school quality basing on the Thailand National Basic Education criterion, and 2) satisfaction of related people in the system. The Feedback data received was from school internal-control evaluation, and CFA results. It was found that the model had a good level of goodness of fit to empirical data when considering the goodness of fit index. Key words:   administrative system, internal control, secondary schools.

Modeling one complete versus triplicate analyses in low template DNA typing

There are generally two strategies for low template DNA typing: the complete strategy, which uses all available DNA in a single PCR and subsequent typing, and the consensus strategy, in which the biological sample is divided into two or more aliquots and the genotype profile is determined by consensus from these "replicates." In this study, the consensus and complete strategies are compared by a statistical approach in terms of the accuracy of obtaining the correct genotype at a single locus for single source samples. Logistic models were employed to describe the allele drop-out and drop-in events. The parameters of the models were estimated with empirical or hypothetical data. The probabilities of obtaining the true genotype and the chances to observe drop-out and drop-in alleles were estimated and compared for both strategies. Consistent with a previous experimental study, this study found that, with relatively high input DNA (e.g., ≥ 100 pg), the complete strategy performs better than the consensus strategy to obtain the true genotype and the complete strategy will display less dropped out alleles. The consensus strategy had less drop-in alleles for ≤ 100 pg DNA samples. Moreover, the limitations of the logistic models were discussed. Ideal models with better fit of empirical data approximating casework conditions were proposed for future studies.