Dynamic Data Analysis Research Articles

Sports Teaching Material Selection in Dynamic Analysis of Big Data--Taking Football Teaching as a Case

The selection of PE teaching materials is one of the key factors for the success of PE teaching.The traditional physical education teaching material evaluation procedure is tedious, and theevaluation result has certain error. Taking the selection and training of football players in HebeiGEO University as an example, this paper introduces the analysis method of big data and datafusion to study the selection of sports teaching materials. The experimental results show that theevaluation results of the proposed method are more accurate than the traditional methods, andthe iteration period is shorter. This method has a certain ability test value for the selection ofphysical education teaching materials.

Survey research on dynamic CTA data analysis for peripheral artery occlusive disease

The purpose of this study is to review the research which was mainly about the evaluation the vascular stenosis or muscle ischemia to assist in the diagnosis of peripheral arterial occlusive disease using dynamic CTA data. With the recent rapid evolution of multislice computed tomography scanners, runoff CTA of the peripheral artery has become a widely used diagnostic option in patients with PAOD. Furthermore the dynamic CTA of the lower extremity provides improved arterial contrast enhancement and a higher diagnostic confidence for assessment of stenosis and occlusion than runoff CTA. The method, the data characteristic and the conclusion of all the qualitative and quantitative research were covered in this study. Finally, the future research direction of the dynamic CTA analysis to assist diagnosis was given.

A Fast Tunable Blurring Algorithm for Scattered Data

A blurring algorithm with linear time complexity can reduce the small-scale content of data observed at scattered locations in a spatially extended domain of arbitrary dimension. The method works by forming a Gaussian interpolant of the input data, and then convolving the interpolant with a multiresolution Gaussian approximation of the Green's function to a differential operator whose spectrum can be tuned for problem-specific considerations. Like conventional blurring algorithms, which the new algorithm generalizes to data measured at locations other than a uniform grid, applications include deblurring and separation of spatial scales. An example illustrates a possible application toward enabling importance sampling approaches to data assimilation of geophysical observations, which are often scattered over a spatial domain, since blurring observations can make particle filters more effective at state estimation of large scales. Another example, motivated by data analysis of dynamics like ocean eddies that have strong separation of spatial scales, uses the algorithm to decompose scattered oceanographic float measurements into large-scale and small-scale components.

Online Robust Principal Component Analysis With Change Point Detection

Robust principal component analysis (PCA) is a key technique for dynamical high-dimensional data analysis, including background subtraction for surveillance video. Typically, robust PCA requires all observations to be stored in memory before processing. The batch manner makes robust PCA inefficient for big data. In this paper, we develop an efficient online robust PCA method, namely, online moving window robust principal component analysis (OMWRPCA). Unlike the existing algorithms, OMWRPCA can successfully track not only slowly changing subspaces but also abruptly changing subspaces. Embedding hypothesis testing into the algorithm enables OMWRPCA to detect change points of the underlying subspaces. Extensive numerical experiments, including real-time background subtraction, demonstrate the superior performance of OMWRPCA compared with other state-of-the-art approaches.

Multi-channel scheduling analysis of dynamic data in wireless networks oriented big data

Multi-channel scheduling analysis of dynamic data in wireless networks oriented big data

Multi-channel scheduling analysis of dynamic data in wireless networks oriented big data

In order to reduce the running time of dynamic data multi-channel scheduling in wireless networks, a multi-channel scheduling analysis method based on partially observable Markov decision process (POMDP) for dynamic data of wireless networks is proposed. Firstly, the data type and dynamic data transmission process in wireless network are analysed. According to the node's request arrival rate and service rate, the network state transition probability and observation probability are calculated. Load balancing is used as the performance optimisation target of dynamic data multi-channel in wireless network, calculate its performance function. By calculating the observation probability and performance function, the dynamic data multi-channel scheduling analysis for big data wireless networks is finally realised. The experimental results show that the proposed method has high data transmission efficiency and low packet loss rate, and the scheduling operation time is relatively short. The effectiveness of the proposed method is verified.

Determination of effective frequency range excited by falling weight deflectometer loading history for asphalt pavement

Falling weight deflectometer (FWD) tests have been widely used to evaluate the layer properties of in-situ asphalt pavement structures. The FWD loading is essentially impulse loading and can excite a wide range of frequency components. This study is to determine the effective frequency range excited by FWD loading through the dynamic back-calculation analysis of FWD data. The FWD deflections were calculated using the spectral element method for asphalt pavements under various field measured FWD loading histories. The calculated deflections were then used as the measured ones to back-calculate the properties of asphalt concrete layers using the dynamic back-calculation procedure. The viscoelastic behaviour of asphalt concrete was characterized using the modified Havriliak-Negami model. For each set of FWD data, several sets of back-calculated model coefficients were obtained by inputting different seed values of model coefficients in the back-calculation procedure. The results show that although all the back-calculated and measured deflection curves are in good agreement, the values of the back-calculated model coefficients can be significantly different and the back-calculated master curves match well only in a specific frequency range that is the effective frequency range excited by FWD loading. The effective frequency ranges for various pavement structures and FWD loading histories were determined. The results show that a higher loading peak level can excite a wider effective frequency range, or more specifically the greater upper limit, regardless of the pavement structures. In general, the effective frequency range excited by a FWD loading history is 5 ~ 65 Hz for asphalt pavement. The existence of effective frequency range indicates that the information contained in FWD data is not sufficient to capture the asphalt concrete properties over the entire frequency domain.

Topic-Based Influence Computation in Social Networks Under Resource Constraints

As social networks are constantly changing and evolving, methods to analyze dynamic social networks are becoming more important in understanding social trends. However, due to the restrictions imposed by the social network service providers, the resources available to fetch the entire contents of a social network are typically very limited. As a result, analysis of dynamic social network data requires maintaining an approximate copy of the social network for each time period, locally. In this paper, we study the problem of dynamic network and text fetching with limited probing capacities, for identifying and maintaining influential users as the social network evolves. We propose an algorithm to probe the relationships (required for global influence computation) as well as posts (required for topic-based influence computation) of a limited number of users during each probing period, based on the influence trends and activities of the users. We infer the current network based on the newly probed user data and the last known version of the network maintained locally. Additionally, we propose to use link prediction methods to further increase the accuracy of our network inference. We employ PageRank as the metric for influence computation. We illustrate how the proposed solution maintains accurate PageRank scores for computing global influence, and topic-sensitive weighted PageRank scores for topic-based influence. The latter relies on a topic-based network constructed via weights determined by semantic analysis of posts and their sharing statistics. We evaluate the effectiveness of our algorithms by comparing them with the true influence scores of the full and up-to-date version of the network, using data from the micro-blogging service Twitter. Results show that our techniques significantly outperform baseline methods and are superior to state-of-the-art techniques from the literature.

Nexus Among Economic Growth, Education, Health, and Environment: Dynamic Analysis of World-Level Data.

The aim of current study is to examine the nexus among economic growth, education, health issues, and carbon emission for the panel of 161 countries. Education and health have confirmed insignificant coefficients for economic growth and carbon emission, which mention that higher education and better health conditions are not useful for boosting economic development and for controlling environmental degradation process. Empirical estimations have reported that higher capital investment leads to increase the economic process and carbon emission. Higher educational standard and capital investment helps to control the health issues, in the long- and short-run. On contrary, higher carbon emission creates health issues. The given results can provide support to the economic, social, and environmental policy makers during policy decisions. For example, the study suggests green financing and low carbon economy concept; the government and industries have to increase the investment on modern, energy efficient, and green technologies, which are useful for economic development, as well as to control the environmental degradation process.

A Hybrid Genetic Algorithm Based Rainfall Prediction Model Using Deep Neural Network

This paper is intended to design and develop an efficient prediction algorithm in the context of forecasting in the precipitation as well as the intensity of the rainfall in a local region over a relative short period of time. Despite of several existing addressing the problem of forecasting rainfall, still it is remind as an open challenge in the context of dynamic data acquisition and aggression. Prediction of rain is main application of science and technology to predict the state of the atmosphere. The main objective of the study is to develop a Genetic Algorithm based approach that utilize dimensionality reduction technique and Multi-layer Perceptron for efficient and dynamic analysis of real time data. Furthermore a deep neural networks based framework is proposed to predict rainfall of a certain region. A Hybrid genetic algorithm presents a novel solution to predicting the rainfall in certain area or different regions by using Deep Neural Network.

Multitasking and Dual Motivational Systems: A Dynamic Longitudinal Study

AbstractThis study further explores the myth of media multitasking: that is, why people increasingly media multitask despite its known harmful effects on performance. Building on previous research on the emotional gratifications of media multitasking and guided by the dynamic motivational activation (DMA) approach, this study specifies emotional gratifications in terms of positive and negative emotions, as well as their underlying appetitive and aversive motivational changes. Using a dynamic panel analysis of longitudinal experience sampling data collected from 71 adolescents (ages 11–17; 61% girls) over 2 weeks, this study identifies several dynamic reciprocal impacts of media multitasking and the dual motivational systems. As predicted by DMA, media multitasking coactivates both the appetitive and aversive motivational systems, and increases both positive and negative emotions; interestingly, only the appetitive system goes on to determine subsequent media multitasking.

REDUCING ENERGY INTENSITY AND INSTITUTIONAL ENVIRONMENT: A CROSS COUNTRY ANALYSIS

The article analyses the way the quality of institutions affects the performance of energy saving policies. Based on the analysis of dynamic panel data for 69 countries over the period from 2002 to 2012, using Arellano-Bond approach, we have shown that the elasticity of energy consumption by price of energy depends on institutional factors. We also demonstrated that absolute values of the said indicators are higher in OECD than CIS countries over the whole data sample, which is explained by a higher quality of institutions. Similar valuations and calculations have been produced for the industrial sector as well as the production sphere. The energy consumption in the industrial sector has proved to be more sensitive to the quality of institutions than in production sphere as a whole. We have also performed a general analysis of trends of GDP energy consumption for a number of countries pointing out that a growth of energy prices enhances energy saving processes.Keywords: energy intensity, cross-country analysis, Arellano-Bond approach.JEL Classifications: D02, E02, Q43DOI: https://doi.org/10.32479/ijeep.8259

Dynamic Ensemble Selection and Data Preprocessing for Multi-Class Imbalance Learning

Class imbalance refers to classification problems in which many more instances are available for certain classes than for others. Such imbalanced datasets require special attention because traditional classifiers generally favor the majority class which has a large number of instances. Ensemble of classifiers has been reported to yield promising results. However, the majority of ensemble methods applied to imbalance learning are static ones. Moreover, they only deal with binary imbalanced problems. Hence, this paper presents an empirical analysis of Dynamic Selection techniques and data preprocessing methods for dealing with multi-class imbalanced problems. We considered five variations of preprocessing methods and 14 Dynamic Selection schemes. Our experiments conducted on 26 multi-class imbalanced problems show that the dynamic ensemble improves the AUC and the [Formula: see text]-mean as compared to the static ensemble. Moreover, data preprocessing plays an important role in such cases.

Bayesian pharmacokinetic modeling of dynamic contrast-enhanced magnetic resonance imaging: validation and application

Tracer-kinetic analysis of dynamic contrast-enhanced magnetic resonance imaging data is commonly performed with the well-known Tofts model and nonlinear least squares (NLLS) regression. This approach yields point estimates of model parameters, uncertainty of these estimates can be assessed e.g. by an additional bootstrapping analysis. Here, we present a Bayesian probabilistic modeling approach for tracer-kinetic analysis with a Tofts model, which yields posterior probability distributions of perfusion parameters and therefore promises a robust and information-enriched alternative based on a framework of probability distributions. In this manuscript, we use the quantitative imaging biomarkers alliance (QIBA) Tofts phantom to evaluate the Bayesian tofts model (BTM) against a bootstrapped NLLS approach. Furthermore, we demonstrate how Bayesian posterior probability distributions can be employed to assess treatment response in a breast cancer DCE-MRI dataset using Cohen’s d. Accuracy and precision of the BTM posterior distributions were validated and found to be in good agreement with the NLLS approaches, and assessment of therapy response with respect to uncertainty in parameter estimates was found to be excellent. In conclusion, the Bayesian modeling approach provides an elegant means to determine uncertainty via posterior distributions within a single step and provides honest information about changes in parameter estimates.

経営者の行動を導くセルフサービスBI “Tableau"による動的データ分析 LEC会計大学院における「マネジメント・シミュレーション」の実践報告

経営者の行動を導くセルフサービスBI “Tableau"による動的データ分析 LEC会計大学院における「マネジメント・シミュレーション」の実践報告

Nanoparticle Loading of Unentangled Polymers Induces Entanglement-Like Relaxation Modes and a Broad Sol-Gel Transition.

We combine molecular dynamics simulations, imaging and data analysis, and the Green-Kubo summation formula for the relaxation modulus G(t) to elicit the structure and rheology of unentangled polymer-nanoparticle composites distinguished by small NPs and strong NP-monomer attraction, εNPM ≫ kBT. A reptation-like plateau emerges in G(t) beyond a terminal relaxation time scale as the volume fraction, cNP, of NPs increases, coincident with a structure transition. A condensed phase of NP-aggregates forms, tightly interlaced with thin sheets of polymer chains, the remaining phase consisting of free chains void of NPs. Rouse mode analyses are applied to the two individual phases, revealing that long-wavelength Rouse modes in the aggregate phase are the source of reptation-like relaxation. Imaging reveals chain motion confined within the thin sheets between NPs and exhibits a 2D analogue of classical reptation. In the NP-free phase, Rouse modes relax indistinguishable from a neat polymer melt. The Fourier transform of G(t) reveals a sol-gel transition across a broad frequency spectrum, tuned by cNP and εNPM above critical thresholds, below which all structure and rheological transitions vanish.

MRI-Driven PET Image Optimization for Neurological Applications.

Positron emission tomography (PET) and magnetic resonance imaging (MRI) are established imaging modalities for the study of neurological disorders, such as epilepsy, dementia, psychiatric disorders and so on. Since these two available modalities vary in imaging principle and physical performance, each technique has its own advantages and disadvantages over the other. To acquire the mutual complementary information and reinforce each other, there is a need for the fusion of PET and MRI. This combined dual-modality (either sequential or simultaneous) could generate preferable soft tissue contrast of brain tissue, flexible acquisition parameters, and minimized exposure to radiation. The most unique superiority of PET/MRI is mainly manifested in MRI-based improvement for the inherent limitations of PET, such as motion artifacts, partial volume effect (PVE) and invasive procedure in quantitative analysis. Head motion during scanning significantly deteriorates the effective resolution of PET image, especially for the dynamic scan with lengthy time. Hybrid PET/MRI device can offer motion correction (MC) for PET data through MRI information acquired simultaneously. Regarding the PVE associated with limited spatial resolution, the process and reconstruction of PET data can be further optimized by using acquired MRI either sequentially or simultaneously. The quantitative analysis of dynamic PET data mainly relies upon an invasive arterial blood sampling procedure to acquire arterial input function (AIF). An image-derived input function (IDIF) method without the need of arterial cannulization, can serve as a potential alternative estimation of AIF. Compared with using PET data only, combining anatomical or functional information from MRI for improving the accuracy in IDIF approach has been demonstrated. Yet, due to the interference and inherent disparity between the two modalities, these methods for optimizing PET image based on MRI still have many technical challenges. This review discussed upon the most recent progress, current challenges and future directions of MRI-driven PET data optimization for neurological applications, with either sequential or simultaneous acquisition approach.

FastqCleaner: an interactive Bioconductor application for quality-control, filtering and trimming of FASTQ files

BackgroundExploration and processing of FASTQ files are the first steps in state-of-the-art data analysis workflows of Next Generation Sequencing (NGS) platforms. The large amount of data generated by these technologies has put a challenge in terms of rapid analysis and visualization of sequencing information. Recent integration of the R data analysis platform with web visual frameworks has stimulated the development of user-friendly, powerful, and dynamic NGS data analysis applications.ResultsThis paper presents FastqCleaner, a Bioconductor visual application for both quality-control (QC) and pre-processing of FASTQ files. The interface shows diagnostic information for the input and output data and allows to select a series of filtering and trimming operations in an interactive framework. FastqCleaner combines the technology of Bioconductor for NGS data analysis with the data visualization advantages of a web environment.ConclusionsFastqCleaner is an user-friendly, offline-capable tool that enables access to advanced Bioconductor infrastructure. The novel concept of a Bioconductor interactive application that can be used without the need for programming skills, makes FastqCleaner a valuable resource for NGS data analysis.

Rethinking behaviour change: a dynamic approach in social marketing

PurposeWhile awareness of social, health and environmental consequences of our collective action are growing, additional efforts are required to deliver the changes needed to affect the greater good. A review of the literature indicates that research efforts may be misdirected. Drawing from empirical data where a total of 161 caregivers reported changes in their child’s walking behaviour following a month long social marketing program, the purpose of this paper is to illustrate differences between behaviour and behaviour change.Design/methodology/approachData analyses involved use of multiple linear regression on static followed by dynamic measures of behaviour and behavioural change and their respective determinants. The static model used variables reported by caregivers after program participation, while the dynamic measures used change scores for all variables reported (T2-T1).FindingsResults from the static model showed that only intentions and barriers explained behaviour at Time point 2. In contrast, findings from the dynamic data analysis indicated that a change in injunctive norms (important others’ approval of the child walking to school) explained a change in walking to and from school behaviour. Taken together, the results of the current paper suggest research attention needs to be directed towards dynamic methodologies to re-centre research attention on behavioural change and not behaviour, which dominates current practice.Originality/valueThis paper offers a foundational step to support the research community to redirect research efforts from understanding behaviour to focussing research design and theoretical development on behavioural change. Theories of behaviour change are needed to affect the greater good.

Dynamic Shrinkage Processes

SummaryWe propose a novel class of dynamic shrinkage processes for Bayesian time series and regression analysis. Building on a global–local framework of prior construction, in which continuous scale mixtures of Gaussian distributions are employed for both desirable shrinkage properties and computational tractability, we model dependence between the local scale parameters. The resulting processes inherit the desirable shrinkage behaviour of popular global–local priors, such as the horseshoe prior, but provide additional localized adaptivity, which is important for modelling time series data or regression functions with local features. We construct a computationally efficient Gibbs sampling algorithm based on a Pólya–gamma scale mixture representation of the process proposed. Using dynamic shrinkage processes, we develop a Bayesian trend filtering model that produces more accurate estimates and tighter posterior credible intervals than do competing methods, and we apply the model for irregular curve fitting of minute-by-minute Twitter central processor unit usage data. In addition, we develop an adaptive time varying parameter regression model to assess the efficacy of the Fama–French five-factor asset pricing model with momentum added as a sixth factor. Our dynamic analysis of manufacturing and healthcare industry data shows that, with the exception of the market risk, no other risk factors are significant except for brief periods.