Complete Sample Data Research Articles

BAYES ESTIMATION OF EXPONENTIALLY DISTRIBUTED SURVIVAL DATA UNDER SYMMETRIC AND ASYMMETRIC LOSS FUNCTIONS

In a research study, population data are often not available, so the population parameter is unknown. Meanwhile, knowledge about the population parameter is needed to know the characteristics of the studied population. Therefore, it is needed to estimate the parameter of the population which can be estimated by sample data. There are several methods of parameter estimation which are generally classified into classical and Bayesian method. This research studied the Bayesian parameter estimation method to determine the parameters of the exponentially distributed survival data associated with the reliability measure of the estimates under symmetric and asymmetric loss functions for complete sample data in a closed form. The symmetric loss functions used in this research are Squared Error Loss Function (SELF) and Minimum Expected Loss Function (MELF). The asymmetric loss functions used are the General Entropy Loss Function (GELF) and Linex Loss Function (LLF). Performance of some loss functions used in this research are then compared through numerical simulation to select the best loss function in determining the parameter estimation of the exponentially distributed survival data. We also studied which loss function is best for underestimation and overestimation modeling. Based on simulation results, the Bayes estimates using MELF is the best method to estimate population parameters of the exponentially distributed survival data for the overestimation modeling, while LLF is the best for the underestimation modeling. We provided direct application in a case study of fluorescence lamp survival data. The results show that the best method to estimate the parameter of the standard fluorescence life data is using LLF for underestimation with and MELF for overestimation with .

Online detection of analog signal anomaly based on the evaluation of local trends

Online anomaly detection (AD) of analog signals plays an important role in equipment fault diagnosis and predictive maintenance. However, the signal often deviates slightly from those seen previously in the early stage of equipment failure, and the anomaly is invisible to the human eye. This kind of anomaly belongs to the typical contextual anomaly. Whether this anomaly can be effectively detected determines whether the failure of the equipment can be detected in the early stage, which is of great significance for safety in production. This study aimed to propose an online AD method for the analog signals of the quasi-sine waveform class. The sample similarity in the sliding window was evaluated using a sample trend rather than sample amplitude deviation to detect anomalies based on the principle that the trend of the quasi-sinusoid waveform signal in the adjacent space was similar. Compared with the traditional method, the proposed method was sensitive to contextual anomalies and did not need a complete sample data set for model training. The proposed method was finally validated by three data sets with good results.

Condition Monitoring and Analysis Method of Smart Substation Equipment Based on Deep Learning in Power Internet of Things

An accurate perception of the state of smart substation equipment is a strong guarantee for the reliable operation of the large power grid. This article proposes using deep learning for the device condition monitoring and analysis method in a power internet of things cloud edge collaboration mode. The speeded up robust features (SURF) feature detector is used at the edge of the network to accurately collect the interest points from the image data set, providing a reliable and complete sample data set support for the cloud-based deep learning network. Adding the attention mechanism module to the cloud improves the Yolov5 network model, enhance feature extraction, and increase the monitoring and analysis capabilities of the equipment. The simulation results show that the proposed method has achieved a recall rate of 91.21% and an accuracy rate of 90.54% for insulator fault evaluation indicators.

Analysis of Influencing Factors of New Energy Vehicle Satisfaction Based On Scenario Thinking and Catboost Model

In order to accurately grasp the influencing factors of consumer product satisfaction with new energy vehicles, this paper uses scenario-based thinking as the framework and a large amount of multi-source heterogeneous data as the basis. Dimensional matching is performed on the structured data, and the random forest machine learning method is used to obtain complete sample data to reflect the overall market characteristics; thus, the CatBoost model is trained based on the complete sample data, and the degree of influence of each feature on satisfaction is analyzed. Through research and analysis, it is found that not only the performance of the product itself has a greater impact on product satisfaction, but the characteristics of consumers’ own attributes are also an important factor affecting product satisfaction. Based on the research conclusions of this article, it can provide reference and basis for the improvement of enterprise product satisfaction and precision marketing.

Association between 24-h urinary sodium to potassium ratio and mild cognitive impairment in community-based general population.

To explore the relationship between parameters of Na and K excretion using 24-h urine sample and mild cognitive impairment (MCI) in general population. This is a cross-sectional study. Community-based general population in Emin China. Totally, 1147 subjects aged ≥18 years were selected to complete the study, with a multistage proportional random sampling method. Cognitive status was assessed with Mini Mental State Examination (MMSE) questionnaire and timed 24-h urine specimens were collected. Finally, 561 participants aged ≥35 years with complete urine sample and MMSE data were included for the current analysis and divided into groups by tertiles of 24-h urinary sodium to potassium ratio (24-h UNa/K) as lowest (T1), middle (T2) and highest (T3) groups. The MMSE score was significantly lower in T3, compared with the T1 group (26·0 v. 25·0, P = 0·002), and the prevalent MCI was significantly higher in T3 than in T1 group (11·7 % v. 25·8 %, P < 0·001). In multiple linear regression, 24-UNa/K (β: -0·184, 95 % CI -0·319, -0·050, P = 0·007) was negatively associated with MMSE score. In multivariable logistic regression, compared with T1 group, 24-h UNa/K in the T2 and T3 groups showed 2·01 (95 % CI 1·03, 3·93, P = 0·041) and 3·38 (95 % CI 1·77, 6·44, P < 0·001) fold odds for presence of MCI, even after adjustment for confounders. More augmented results were demonstrated in sensitivity analysis by excluding individuals taking anti-hypertensive agents. Higher 24-h UNa/K is in an independent association with prevalent MCI.

OpenMandible: An open-source framework for highly realistic numerical modelling of lower mandible physiology

ObjectiveComputer modeling of lower mandible physiology remains challenging because prescribing realistic material characteristics and boundary conditions from medical scans requires advanced equipment and skill sets. The objective of this study is to provide a framework that could reduce simplifications made and inconsistency (in terms of geometry, materials, and boundary conditions) among further studies on the topic. MethodsThe OpenMandible framework offers: 1) the first publicly available multiscale model of the mandible developed by combining cone beam computerized tomography (CBCT) and μCT imaging modalities, and 2) a C++ software tool for the generation of simulation-ready models (tet4 and hex8 elements). In addition to the application of conventional (Neumann and Dirichlet) boundary conditions, OpenMandible introduces a novel geodesic wave propagation - based approach for incorporating orthotropic micromechanical characteristics of cortical bone, and a unique algorithm for modeling muscles as uniformly directed vectors. The base intact model includes the mandible (spongy and compact bone), 14 teeth (comprising dentin, enamel, periodontal ligament, and pulp), simplified temporomandibular joints, and masticatory muscles (masseter, temporalis, medial, and lateral pterygoid). ResultsThe complete source code, executables, showcases, and sample data are freely available on the public repository: https://github.com/ArsoVukicevic/OpenMandible. It has been demonstrated that by slightly editing the baseline model, one can study different “virtual” treatments or diseases, including tooth restoration, placement of implants, mandible bone degradation, and others. SignificanceOpenMandible eases the community to undertake a broad range of studies on the topic, while increasing their consistency and reproducibility. At the same time, the needs for dedicated equipment and skills for developing realistic simulation models are significantly reduced.

Practical fluorescence reconstruction microscopy for large samples and low-magnification imaging.

Fluorescence reconstruction microscopy (FRM) describes a class of techniques where transmitted light images are passed into a convolutional neural network that then outputs predicted epifluorescence images. This approach enables many benefits including reduced phototoxicity, freeing up of fluorescence channels, simplified sample preparation, and the ability to re-process legacy data for new insights. However, FRM can be complex to implement, and current FRM benchmarks are abstractions that are difficult to relate to how valuable or trustworthy a reconstruction is. Here, we relate the conventional benchmarks and demonstrations to practical and familiar cell biology analyses to demonstrate that FRM should be judged in context. We further demonstrate that it performs remarkably well even with lower-magnification microscopy data, as are often collected in screening and high content imaging. Specifically, we present promising results for nuclei, cell-cell junctions, and fine feature reconstruction; provide data-driven experimental design guidelines; and provide researcher-friendly code, complete sample data, and a researcher manual to enable more widespread adoption of FRM.

A Combined Deep Learning Method with Attention-Based LSTM Model for Short-Term Traffic Speed Forecasting

Short-term traffic speed prediction is a promising research topic in intelligent transportation systems (ITSs), which also plays an important role in the real-time decision-making of traffic control and guidance systems. However, the urban traffic speed has strong temporal, spatial correlation and the characteristic of complex nonlinearity and randomness, which makes it challenging to accurately and efficiently forecast short-term traffic speeds. We investigate the relevant literature and found that although most methods can achieve good prediction performance with the complete sample data, when there is a certain missing rate in the database, it is difficult to maintain accuracy with these methods. Recent studies have shown that deep learning methods, especially long short-term memory (LSTM) models, have good results in short-term traffic flow prediction. Furthermore, the attention mechanism can properly assign weights to distinguish the importance of traffic time sequences, thereby further improving the computational efficiency of the prediction model. Therefore, we propose a framework for short-term traffic speed prediction, including data preprocessing module and short-term traffic prediction module. In the data preprocessing module, the missing traffic data are repaired to provide a complete dataset for subsequent prediction. In the prediction module, a combined deep learning method that is an attention-based LSTM (ATT-LSTM) model for predicting short-term traffic speed on urban roads is proposed. The proposed framework was applied to the urban road network in Nanshan District, Shenzhen, Guangdong Province, China, with a 30-day traffic speed dataset (floating car data) used as the experimental sample. Results show that the proposed method outperforms other deep learning algorithms (such as recurrent neural network (RNN) and convolutional neural network (CNN)) in terms of both calculating efficiency and prediction accuracy. The attention mechanism can significantly reduce the error of the LSTM model (up to 12.4%) and improves the prediction performance.

Muscular and Cardiorespiratory Fitness are Independently Associated with Metabolic Risk in Adolescents. The HELENA Study

PURPOSE: To examine the independent associations of muscular fitness and cardiorespiratory fitness with clustered metabolic risk in adolescents. METHODS: Data were gathered from the HELENA (Healthy Lifestyle in Europe by Nutrition in Adolescents) cross-sectional study. A total of 709 adolescents (346 boys) from 9 European countries, aged from 12.5 to 17.5 y, with complete muscular fitness, cardiorespiratory fitness and blood sample data, were included in this report. A muscular fitness index was computed as sum of age- and gender-specific z-scores of handgrip strength / body weight and standing broad jump. Cardiorespiratory fitness was estimated using the 20m shuttle run test. Risk factors included in the composite metabolic risk score (sum of age- and gender-specific z-scores) were waist circumference, systolic blood pressure, triglyceride, ratio total cholesterol / high-density lipoprotein cholesterol, and insulin resistance (HOMA). Sex, age, puberty and country were used as confounders. On the basis of body mass index (BMI), adolescents were classed as non-overweight or overweight (including obese). RESULTS: Muscular fitness index was negatively associated with clustered metabolic risk independent of cardiorespiratory fitness (b = -0.246, P < 0.001). Independent of muscular fitness, an inverse association was found between cardiorespiratory fitness and clustered metabolic risk (b = -0.283, P < 0.001). Moreover, the odds ratios for having a high clustered risk (above or equal 1 SD) in the least fit quartile compared with the most fit quartile were 5.9 (95% confidence interval (CI) = 3.2-11.0) and 4.9 (95% CI = 2.5-9.8) for muscular fitness and cardiorespiratory fitness, respectively. Significant differences in metabolic risk across muscular fitness levels persisted among non-overweight (P = 0.007) and overweight participants (P = 0.013). CONCLUSIONS: Our results show that muscular fitness and cardiorespiratory fitness are independently associated with metabolic risk in adolescence. Health promotion policies and physical activity programs for youths should be designed to improve both cardiorespiratory and muscular fitness. The HELENA Study was carried out with the financial support of the European Community Sixth RTD Framework Programme (Contract FOOD-CT-2005-007034).

Analysis of Two Economic Testing Policies in Discrete Time

Two economic testing policies for industrial products are considered in discrete time. Based on the usual total time on test (TTT) method, we develop non-parametric statistical estimation algorithms to determine the optimal testing times, minimizing the expected cost function per unit time and maximizing the expected profit function per unit time, given that the complete sample data of lifetime are available. It is shown numerically in simulation experiments that the resulting estimators in discrete models possess statistical consistency even if the parametric form of lifetime distribution is unknown.

Statistical estimation algorithms for repairs-time limit replacement scheduling under earning rate criteria

In this paper, we formulate two repair-time limit replacement problems with imperfect repair under earning rate criteria with and without discounting. First, the optimal repair-time limits which maximize the long-run average profit rate and the expected total discounted profit over an infinite time horizon are analytically derived. Next, we develop the nonparametric algorithms for estimating the optimal repair-time limits, provided that the complete sample data of repair time are given. The basic idea is to apply the Lorenz statistics and to transform the underlying algebraic problems to the graphical ones. Finally, we present some numerical examples to show that the proposed algorithms can be useful to estimate the profit-based repair limit replacement schedule.

Discrete-time cost analysis for a telecommunication billing application with rejuvenation

Software rejuvenation is a proactive fault management technique that has been extensively studied in the recent literature. In this paper, we focus on an example for a telecommunication billing application considered in [1] and develop the discrete-time stochastic models to estimate the optimal software rejuvenation schedules. More precisely, two software cost models with rejuvenation are formulated via the discrete semi-Markov processes, and the optimal software rejuvenation schedules which minimize the expected costs per unit time in the steady state are derived analytically. Further, we develop statistically nonparametric algorithms to estimate the optimal software rejuvenation schedules, provided that the complete sample data of failure times are given. Then, a new statistical device, called discrete total time on test statistics, is introduced. Finally, we examine asymptotic properties for the statistical estimation algorithms proposed in this paper through a simulation experiment.

ANALYSIS OF PERIODIC SOFTWARE REJUVENATION POLICY BASED ON NET PRESENT VALUE APPROACH

In this paper, we consider a cost model with periodic rejuvenation and derive the optimal software rejuvenation policy which minimizes the expected total discounted cost over an infinite time horizon, based on the familiar net present value approach. Further, since the failure time distribution can not be easily estimated from a few data samples in practice, we develop a statistically non-parametric algorithm to estimate the optimal periodic rejuvenation policy, provided that the complete sample data of failure times is given. Numerical examples based on the Monte Carlo simulation are illustrated to show asymptotic property of the proposed estimator.

Estimating Software Rejuvenation Schedules in High-Assurance Systems

Software rejuvenation is a preventive maintenance technique that has been extensively studied in recent literature. In this paper, we extend the classical result by Huang et al. (1995), and in addition propose a modified stochastic model to generate the software rejuvenation schedule. More precisely, the software rejuvenation models are formulated via the semi-Markov reward process, and the optimal software rejuvenation schedules are derived analytically in terms of the reward rate. In particular, we consider the two special cases: steady-state availability and expected cost per unit time in the steady state. Further, we develop non-parametric algorithms to estimate the optimal software rejuvenation schedules, provided that the statistically complete (unsensored) sample data of failure time is given. In numerical examples, we compare two models from the viewpoints of system availability and economic justification, and examine asymptotic properties for the statistical estimation algorithms.

Projecting From Advance Data Using Propensity Modeling: An Application to Income and Tax Statistics

This article proposes and evaluates two new methods of reweighting preliminary data to obtain estimates more closely approximating those derived from the final data set. In our motivating example, the preliminary data are an early sample of tax returns, and the final data set is the sample after all tax returns have been processed. The new methods estimate a predicted propensity for late filing for each return in the advance sample and then poststratify based on these propensity scores. Using advance and complete sample data for 1982, we demonstrate that the new methods produce advance estimates generally much closer to the final estimates than those derived from the current advance estimation techniques. The results demonstrate the value of propensity modeling, a general-purpose methodology that can be applied to a wide range of problems, including adjustment for unit nonresponse and frame undercoverage as well as statistical matching.

Computational Techniques For Maximum Likelohood Estimation Of Three-Parameter Weibull Distribution

Two improved numerical techiques are employed to compute MLE for the paraıneters of the 3‘parameter V eibuU distribution using complete sample data of large sample size and of large-valued inspection points. For the solution of estimates a new minimal functional is defined and optimized by the descent method and the method of conjugate gradients. The result of both techniques are compared. Asymptotic variance-covariance matrix of estimates for the complete sample is inciuded.

Some simple tests of mixtures with symmetrical components

Methods are described for testing whether an observed sample is consistent with it being from a mixture (in unknown proportions) of two specified symmetrical populations. The tests use only very simple sample statistics. Possible tests for use when more complete sample data are available are also mentioned.

Isotopes’ Radiocarbon Measurements VIII

This date list presents results of samples measured at ISOTOPES during 1968 and 1969 and several measurements made previously for which complete sample data has recently been received.

Isotopes’ Radiocarbon Measurements VII

The following list presents dates on a portion of the samples measured at ISOTOPES during 1967 and 1968 and measurements made previously for which either complete sample data has been recently received, or, in some cases, deferred due to the editorial load in preparing the definitive list.

Isotopes' Radiocarbon Measurements VI

This list presents dates on a portion of the samples measured at ISOTOPES since publication of Isotopes V, and for previously analyzed samples for which complete sample data are now available.Samples were analyzed by gas proportional counting of carbon dioxide at 3 atm pressure in counters of 1 1. volume. Each sample was assayed twice, in different counters and on different days.Procedures employed in sample preparation and age calculations have been described previously (Isotopes V).