Pivotal Method Research Articles

Using auxiliary data to rationalize smartphone-based pre-harvest forest mensuration

Abstract Accurate mensuration of forest stands for pre-harvest planning will pose high costs if carried out by a professional forester as an on-site evaluation. The costs could be reduced if a person with limited mensuration expertise could collect the required data using a smartphone-based system such as TRESTIMA® Forest Inventory System. Without prior information, the field sample with sufficient number of measurement points over the whole stand should be selected, so that the entire variation will be covered. We present and test a rational framework based on selecting the sampling locations according to auxiliary data. As auxiliary variables, we use various spatial data sources indicating forests’ structural or spectral variation, as well as previously predicted inventory variables. We construct two variants of sampling schemes based on the local pivotal method, weighted by the auxiliary data, and compare the results to simple random sampling (SRS) with corresponding sample sizes. According to our findings, the benefits of auxiliary data depend on the considered stand, species and timber assortment. The use of auxiliary data leads generally to improved results and up to three times higher efficiency (i.e. lower variance) as compared with SRS. We conclude that the framework of applying auxiliary data has high capabilities in rationalizing the sampling efforts with little drawbacks, consequently providing potential to improve the results with similar sample size and possibility to use of non-specialists for the pre-harvest inventory.

Estimation and prediction for a new Pareto-type distribution under progressive type-II censoring

In this paper, we consider the problems of estimating the unknown parameters as well as predicting the failure times of the removed units in multiple stages of the progressively censored sample coming from a new Pareto-type distribution. First, maximum likelihood, least squares and Bayesian methods (Lindley’s approximation and Markov chain Monte Carlo) are applied for estimating the parameters involved in this model. For predicting the failure times of the removed units, different prediction methods including maximum likelihood, best unbiased, conditional median and Bayesian prediction methods are adopted. We also develop prediction intervals of future lifetimes using pivot and highest conditional density methods. Comprehensive Monte Carlo simulations are performed to assess the performance of the estimation methods as well as prediction methods. Finally, analysis of a real data set representing the duration of remission of leukemia patients who were treated by a specific drug is performed for illustration purposes.

Puromycin-Modified Silica Microsphere-Based Nascent Proteomics Method for Rapid and Deep Nascent Proteome Profile.

Nascent proteome is crucial in directly revealing how the expression of a gene is regulated on a translation level. In the nascent protein identification, puromycin capture is one of the pivotal methods, but it is still facing the challenge in the deep profiling of nascent proteomes due to the low abundance of most nascent proteins. Here, we describe the synthesis of puromycin-modified silica microspheres (PMSs) as the sorbent of dispersive solid-phase microextraction and the establishment of the PMS-based nascent proteomics (PMSNP) method for efficient capture and analysis of nascent proteins. The modification efficiency of puromycin groups on silica microspheres reached 91.8% through the click reaction. After the optimization and simplification of PMSNP, more than 3500 and 3900 nascent proteins were rapidly identified in HeLa cells and mouse brains within 13.5 h, respectively. The PMSNP method was successfully applied to explore changes in the translation process in a biological stress model, namely, the lipopolysaccharide-stimulated HeLa cells. Biological functional analyses revealed the unique characters of the nascent proteomes and exhibited the superiority of the PMSNP in the identification of low abundance and secreted nascent proteins, thus demonstrating the sensitivity and immediacy of the PMSNP method.

Workload Optimization by Horizontal Aggregation in SQL for Data Mining Analysis

Preparing a data set for analysis is generally the most time consuming task in a data mining project, requiring many complex SQL queries, joining tables, and aggregating columns. Existing SQL aggregations have limitations to prepare data sets because they return one column per aggregated group. In general, a significant manual effort is required to build data sets, where a horizontal layout is required. We propose simple, yet powerful, methods to generate SQL code to return aggregated columns in a horizontal tabular layout, returning a set of numbers instead of one number per row. This new class of functions is called horizontal aggregations. Horizontal aggregations build data sets with a horizontal denormalized layout (e.g., point-dimension, observation variable, instance-feature), which is the standard layout required by most data mining algorithms. We propose three fundamental methods to evaluate horizontal aggregations: ? CASE: Exploiting the programming CASE construct; ? SPJ: Based on standard relational algebra operators (SPJ queries); ? PIVOT: Using the PIVOT operator, which is offered by some DBMSs. Experiments with large tables compare the proposed query evaluation methods. Our CASE method has similar speed to the PIVOT operator and it is much faster than the SPJ method. In general, the CASE and PIVOT methods exhibit linear scalability, whereas the SPJ method does not. For query optimization the distance computation and nearest cluster in the k-means are based on SQL. Workload balancing is the assignment of work to processors in a way that maximizes application performance. The process of load balancing can be generalized into four basic steps: 1. Monitoring processor load and state; 2. Exchanging workload and state information between processors; 3. Decision making; 4. Data migration. The decision phase is triggered when the load imbalance is detected to calculate optimal data redistribution. In the fourth and last phase, data migrates from overloaded processors to under-loaded ones.

Interval estimation for inverse Gaussian distribution

AbstractIn this paper, we consider interval estimation for the inverse Gaussian (IG) distribution. Using generalized pivotal quantity method, we derive the generalized confidence intervals (GCIs) for the model parameters and some quantities such as the quantile, the reliability function of the lifetime, the failure rate function, and the mean residual lifetime. We verify that the GCI of the scale parameter is the same as its commonly used exact CI. We also obtain the generalized prediction intervals (GPIs) for future failure times based on the observed failure data set. In addition, we get the GCI for the reliability of the stress–strength model when the stress and strength variables follow the IG distributions with different parameters. We compare the proposed GCIs and GPIs with the Wald CIs and bootstrap‐ CIs by simulation. The simulation results show that the proposed GCIs and GPIs are superior to the Wald CIs and the bootstrap‐p CIs in terms of the coverage probability. Finally, two examples are used to illustrate the proposed procedures.

Traditional Chinese Medicine (TCM) safety evaluation and pharmacovigilance

<p indent="0mm">In recent years, there has been an increase in the number of adverse reactions/events associated with Traditional Chinese Medicine (TCM). This could be due to the passive state of the safety and risk management of TCM, which has not only led to adverse effects but also restricted the healthy and long-term development of industrial disciplines in the field of TCM. To overcome the problems associated with the safe use of TCM, we propose to speed up the establishment of a pharmacovigilance technology system in line with the characteristics of TCM, and ensure the transformation of TCM safety risk management from a passive response to active prevention and control at the earliest. Therefore, this paper systematically discusses: (1) The basic context and issues related to drug safety, (2) main challenges in the safe clinical use of TCM, and (3) pivotal pharmacovigilance methods and approaches applicable for Chinese medicine products. Drug-induced liver injury (DILI) triggered by <italic>Polygonum multiflorum</italic> and other TCMs is a current worldwide concern. Thus, we established an integrated pharmacovigilance approach of “objective identification-mechanism analysis-risk prevention and control” to solve this problem. The established pharmacovigilance technology system applied an “integrated evidence chain-based causality evaluation method” and “three-level diagnostic criteria” for the causality assessment of DILI. A new toxicology evaluation model and method, named as “disease-syndrome-based toxicology”, was developed for the safety evaluation of TCM. This system was designed for the prevention and control of TCM safety risks based on the identification of susceptible individuals, control of factors that enhance susceptibility to adverse reactions, and clinical precision medicine. Moreover, this system enables multi-source data fusion, sharing, and co-creation for DILI active monitoring and evaluation to provide data support for safety supervision and clinical drug decision making. Additionally, it provided the first online safe drug information inquiry platform, named “Inquiring Drug Safety”, for online data sharing and co-creation. Moreover, it helped construct a new model for the prevention mechanisms of TCM safety risks in people. The proposed system has been successfully used in the formulation of strategies to prevent and control safety risks of TCM and related preparations, such as <italic>Polygonum multiflorum</italic> and Rhubarb. Furthermore, this system led to the development of the first technical standard for the safe use of TCM: “Guidelines for safe use of Polygoni Multiflori Radix”. In conclusion, the established pharmacovigilance technology system overcomes the problems of causality evaluation, as well as risk prevention and control of DILI caused by TCM. Furthermore, it supports the clinical rational and safe use of TCM, and makes important contributions to ensure the healthy and sustainable development of TCM disciplines and its industry.

Quantitative determination of dexamethasone sodium phosphate in bulk and pharmaceuticals at suitable pH values using the spectrophotometric method.

Dexamethasone sodium phosphate (DSP) is an ester of dexamethasone with anti-inflammatory action. This study provides new insights to develop a simple, precise, and accurate spectrophotometric method for the quantitative determination of DSP in bulk and pharmaceuticals. The method was validated before being applied to determine the DSP in six pharmaceutical injection forms from different companies. DSP is soluble in phosphate buffer, so it was used as a solvent, and a pH of 6 was found to be suitable for determination purposes. The DSP solution was scanned in the ultraviolet range (200–400 nm) using a double-beam spectrophotometer with a 1-cm quartz cell. The wavelength (λ max) of DSP was set at 242.5 nm, following the Beer–Lambert law for concentrations from 2 to 50 μg/ml. Dexa AIWA (Germany) showed the best results, being very close to the bulk value with no significant variation. Similarly, Dexamed (Cyprus) and HEMAZON (Syria) showed no significant differences from the bulk; however, the three remaining injections, DEXAKAL (India), DEXABRU (India), and DEXARON (China), showed significant variations from the bulk. Estimated limit of detection and limit of quantitation values for DSP were 0.83 and 2.5 μg/ml, respectively, with a regression coefficient of 0.999. Recovery studies were then used to determine the accuracy of the suggested method. The percentage of recovery was found to be 98.58%–102.52%. All results are suggesting a pivotal method for the routine analysis of DSP both in pure form and the commercially pharmaceutical forms.

Active regional surveillance for early detection of exotic/emerging pathogens of swine: A comparison of statistical methods for farm selection

In this study, five spatially balanced sampling methods, i.e., generalized random-tessellation stratified (GRTS), local pivotal method (LPM), spatially correlated Poisson sampling (SCPS), local cube method (LCUBE), and balanced acceptance sampling (BAS) were compared to simple random sampling (SRS) based on a livestock disease transmission model on a hypothetical region (195 km × 300 km) populated with 6000 farms in terms of the probability of detection by sample size. Given a fixed sample size, four of the five spatially balanced sampling methods provided better performance than SRS, i.e., higher probabilities of detecting at least one infected farms over a range of regional prevalence evaluated (1%–5%). That is, for any given probability of detection, spatially balanced methods required testing fewer farms than SRS. In an era of pandemics, active regional surveillance for early detection of emerging pathogens becomes urgent, yet shrinking budgets impose intractable constraints. The better performance and higher efficiency of spatially balanced sampling methods suggests a potential improvement in regional livestock disease surveillances and a partial solution to the challenge of affordable surveillance.

Degradation data analysis based on gamma process with random effects

This paper focuses on investigating the Gamma degradation model with random effects. A generalized p-value procedure is proposed to test whether there exist some heterogeneities among the degradation processes of different units. Using the Cornish-Fisher expansion, an approximate confidence interval (CI) is obtained for the shape parameter. The generalized confidence intervals (GCIs) are derived for model parameters and commonly used reliability metrics (e.g., the quantile, the reliability function of the lifetime) based on the generalized pivotal quantity method. Those inference procedures are also extended to the accelerated degradation case. The performances of the proposed GCIs are assessed by Monte Carlo simulations. In the simulation, we compared our methods with the Wald CIs and bootstrap-p CIs under moderate and large sample sizes. It is found that the performance of the GCI procedures is better than the Wald CIs and bootstrap-p CIs in terms of coverage probabilities. Finally, the proposed procedures are illustrated by two examples.

Heuristic Analysis of CIC Filter Design for Next-Generation Wireless Applications

This paper describes a novel multistage CIC filter which plays a vital role in software-defined radio (SDR) applications. In order to operate the baseband signal, wireless networking standards demand different sample rates. As a result, sample rate conversion (SRC) turns out as a pivotal method of SDR. Thus, cascaded integrator comb (CIC) filter is a distinct kind of linear-phase FIR filter which can be used as a decimation filter for SRC operation. The architecture of this filter is multiplierless with low passband droop; hence, it requires less area in contrast to other decimation filters. In this brief, the fundamental structure of CIC filter is examined with various parallel prefix adders and also the significant parameters of the CIC filter for different adders are exemplified. The proposed CIC filter with various parallel prefix adders has been designed in Verilog HDL using Xilinx ISE 14.7 and implemented on Kintex7 FPGA. The schemed CIC filter design is compared with the traditional CIC filter in view of area, speed and power consumption. Based on the obtained results on Kintex7 FPGA, the CIC filter with Brent Kung adder outperforms in terms of LUTs by 41.67% and power by 34.78% compared with the classical CIC filter and among the CIC filters using other parallel prefix adders. The proposed CIC uses new polynomial method which provides 33.33% reduction in passband droop and 41.14% reduction in stopband ripple compared to the existing sharpened CIC.

Comparison of the local pivotal method and systematic sampling for national forest inventories

BackgroundThe local pivotal method (LPM) utilizing auxiliary data in sample selection has recently been proposed as a sampling method for national forest inventories (NFIs). Its performance compared to simple random sampling (SRS) and LPM with geographical coordinates has produced promising results in simulation studies. In this simulation study we compared all these sampling methods to systematic sampling. The LPM samples were selected solely using the coordinates (LPMxy) or, in addition to that, auxiliary remote sensing-based forest variables (RS variables). We utilized field measurement data (NFI-field) and Multi-Source NFI (MS-NFI) maps as target data, and independent MS-NFI maps as auxiliary data. The designs were compared using relative efficiency (RE); a ratio of mean squared errors of the reference sampling design against the studied design. Applying a method in NFI also requires a proven estimator for the variance. Therefore, three different variance estimators were evaluated against the empirical variance of replications: 1) an estimator corresponding to SRS; 2) a Grafström-Schelin estimator repurposed for LPM; and 3) a Matérn estimator applied in the Finnish NFI for systematic sampling design.ResultsThe LPMxy was nearly comparable with the systematic design for the most target variables. The REs of the LPM designs utilizing auxiliary data compared to the systematic design varied between 0.74–1.18, according to the studied target variable. The SRS estimator for variance was expectedly the most biased and conservative estimator. Similarly, the Grafström-Schelin estimator gave overestimates in the case of LPMxy. When the RS variables were utilized as auxiliary data, the Grafström-Schelin estimates tended to underestimate the empirical variance. In systematic sampling the Matérn and Grafström-Schelin estimators performed for practical purposes equally.ConclusionsLPM optimized for a specific variable tended to be more efficient than systematic sampling, but all of the considered LPM designs were less efficient than the systematic sampling design for some target variables. The Grafström-Schelin estimator could be used as such with LPMxy or instead of the Matérn estimator in systematic sampling. Further studies of the variance estimators are needed if other auxiliary variables are to be used in LPM.

Spatial Spread Sampling Using Weakly Associated Vectors

Geographical data are generally autocorrelated. In this case, it is preferable to select spread units. In this paper, we propose a new method for selecting well-spread samples from a finite spatial population with equal or unequal inclusion probabilities. The proposed method is based on the definition of a spatial structure by using a stratification matrix. Our method exactly satisfies given inclusion probabilities and provides samples that are very well spread. A set of simulations shows that our method outperforms other existing methods such as the generalized random tessellation stratified or the local pivotal method. Analysis of the variance on a real dataset shows that our method is more accurate than these two. Furthermore, a variance estimator is proposed.

ESTIMASI SELANG KEPERCAYAAN NILAI UJIAN NASIONAL BERBASIS KOMPETENSI BERDASARKAN MODEL REGRESI SEMIPARAMETRIK MULTIRESPON TRUNCATED SPLINE

Confidence interval estimation is important in statistical inference for the parameters of the regression model, but the theory of confidence interval estimation for multi-response semiparametric regression model parameters based on the truncated spline estimator has not been examined. In this study, we estimate the confidence interval of the multi-response semiparametric regression model based on the truncated spline estimator by using pivotal quantity method with the central limit theorem approach. This confidence interval theory is applied to data of competency-based national exam (UNBK) scores in West Nusa Tenggara Province where its UNBK in the lowest position among other provinces in Indonesia. The method used for estimating parameters is weighted least square. The best model is determined based on the Generalized Cross Validation (GCV) minimum value. Based on the estimated 95% confidence interval of parameters of the multi-response truncated spline semiparametric regression model, the results showed that the insignificant factors affecting the UNBK scores were gender and parental education duration while the report card of scores and USBK scores had a positive effect on the UNBK scores but only the UNBK scores of mathematics that report card of scores factor has a negative effect on it.

Tensor Network Representations of Parton Wave Functions.

Tensor network states and parton wave functions are two pivotal methods for studying quantum many-body systems. This work connects these two subjects as we demonstrate that a variety of parton wave functions, such as projected Fermi sea and projected fermionic or bosonic paired states, can be represented exactly as tensor networks. The results can be compressed into matrix product states with moderate bond dimensions so various physical quantities can be computed efficiently. For the projected Fermi sea, we develop an excellent compression scheme with high fidelity using maximally localized Wannier orbitals. Numerical calculations on two parton wave functions demonstrate that our method exceeds commonly adopted MonteCarlo methods in some aspects. It produces energy and correlation function with very high accuracy that is difficult to achieve using MonteCarlo method. The entanglement measures that were almost impossible to compute before can also be obtained easily using our method.

Prediction Methods for Future Failure Times Based on Type-II Right-Censored Samples from New Pareto-Type Distribution

In this paper, we consider the prediction problem of the future failure times based on observed data from two-parameter Pareto-type distribution under Type-II right-censored samples. Different point predictors including best unbiased, maximum likelihood and conditional median predictors of the future failure times are obtained. The corresponding prediction intervals using pivotal quantity, conditional argument and shortest-length based method are also developed. The so-obtained point predictors and prediction intervals are compared via experimental numerical simulation. The optimality criteria considered for comparison purposes are prediction bias and mean square prediction error for point predictors and average length and coverage probability for prediction intervals. Real-life data representing the duration of the failure times of mechanical components as reported by Murthy (in: Wiley series in probability and statistics, Wiley, Hoboken, 2004) are analyzed for illustrative purposes.

Pivotal Inference for the Inverted Exponentiated Rayleigh Distribution Based on Progressive Type-II Censored Data

In this article, the pivotal inference is proposed to estimate the two unknown parameters of the inverse exponentiated Rayleigh distribution based on progressive censored data. We derive the point estimator and construct an interval estimator using the pivotal quantity method. To compare the performance of this proposed method and the traditional maximum likelihood estimation method, a simulation study is conducted. The simulation results show that the proposed method performs better in terms of bias and mean squared error. Finally, a real dataset is used to illustrate the proposed approaches.

Pyrene based chemosensor for carbon dioxide gas – Meticulous investigations and digital image based RGB analysis

A new pyrene Schiff base (KB-4) was synthesized and demonstrated as simple colorimetric chemosensor for carbon dioxide (CO2) gas activated by fluoride anion. The chemical structure of KB-4 was validated through pivotal characterization methods. A combined quantum chemical calculations and spectroscopic techniques were adopted to understand the photophysical properties of KB-4. The sensitivity of KB-4 chemosensor towards various anions is investigated and showed excellent selectivity towards F− anion with a distinct color change. The mechanism of F− anion sensing is due to the complete deprotonation of –NH proton in KB-4 as revealed by 1H NMR titration studies as well as DFT calculations. Subsequently, the in-situ deprotonated KB-4 is employed for CO2 gas sensing. Interestingly, deprotonated KB-4 is responded instantaneously to CO2 gas by exhibiting significant colorimetric and fluorimetric changes with a detection limit of 0.4 ml. Further, digital image based RGB analysis using smartphone is introduced for detection of CO2 gas. The digital images of CO2 gas induced fluorimetric changes are captured using smartphone and the RGB analysis of the images are processed using MATLAB. The intensity variation in the color components as an indicative measure for detection of CO2 gas is demonstrated.

Collaborative Research Project: Developing and Testing a Robot-Assisted Intervention for Children With Autism.

The present work is a collaborative research aimed at testing the effectiveness of the robot-assisted intervention administered in real clinical settings by real educators. Social robots dedicated to assisting persons with autism spectrum disorder (ASD) are rarely used in clinics. In a collaborative effort to bridge the gap between innovation in research and clinical practice, a team of engineers, clinicians and researchers working in the field of psychology developed and tested a robot-assisted educational intervention for children with low-functioning ASD (N = 20) A total of 14 lessons targeting requesting and turn-taking were elaborated, based on the Pivotal Training Method and principles of Applied Analysis of Behavior. Results showed that sensory rewards provided by the robot elicited more positive reactions than verbal praises from humans. The robot was of greatest benefit to children with a low level of disability. The educators were quite enthusiastic about children's progress in learning basic psychosocial skills from interactions with the robot. The robot nonetheless failed to act as a social mediator, as more prosocial behaviors were observed in the control condition, where instead of interacting with the robot children played with a ball. We discuss how to program robots to the distinct needs of individuals with ASD, how to harness robots' likability in order to enhance social skill learning, and how to arrive at a consensus about the standards of excellence that need to be met in interdisciplinary co-creation research. Our intuition is that robotic assistance, obviously judged as to be positive by educators, may contribute to the dissemination of innovative evidence-based practice for individuals with ASD.

ENTROPY-BASED AND NON-ENTROPY-BASED GOODNESS OF FIT TEST FOR THE INVERSE RAYLEIGH DISTRIBUTION WITH PROGRESSIVELY TYPE-II CENSORED DATA

In this paper, the problem of goodness of fit test for the inverse Rayleigh distribution based on progressively Type-II censored samples is studied. We develop two test statistics via entropy and propose one new non-entropy test statistic via a pivotal method. We also study the properties of these test statistics. Critical values are obtained by simulations. Then, we do power analysis of these test statistics against various alternatives under different censoring schemes. We conclude that the tests we proposed perform well against various alternatives, especially for non-monotone hazard alternatives. Finally, one real data set is analyzed.

Application of PCA Analysis and QR Decomposition to Address RFM's Ill-Posedness

Rational function model (&lt;small&gt;RFM&lt;/small&gt;) is the most widely used sensor model in the remote sensing community. However, it suffers from ill-posedness, challenging its feasibility. This problem, i.e., ill-posedness, is mainly caused due to highly correlated coefficients of the &lt;small&gt;RFM&lt;/small&gt;, which magnifies any small perturbations of observations, such as noise and instrumental error. This paper outlines a novel two-step method, called principal component analysis (&lt;small&gt;PCA&lt;/small&gt;)-&lt;small&gt;RFM&lt;/small&gt;, based on the integration of &lt;small&gt;PCA&lt;/small&gt; and QR decomposition. In the first step, the &lt;small&gt;PCA&lt;/small&gt;-&lt;small&gt;RFM&lt;/small&gt; reduces the observational perturbations from the design matrix using the &lt;small&gt;PCA&lt;/small&gt;. In the next step, the &lt;small&gt;RFM&lt;/small&gt;'s coefficients are estimated using a &lt;small&gt;QR&lt;/small&gt; decomposition with column pivoting and least square method. According to the results, the &lt;small&gt;PCA&lt;/small&gt;-&lt;small&gt;RFM&lt;/small&gt; is less sensitive than its rivals to the changes of the ground control point (&lt;small&gt;GCPs&lt;/small&gt;) distribution. Geometrically speaking, in addition, &lt;small&gt;PCA&lt;/small&gt;-&lt;small&gt;RFM&lt;/small&gt; is more accurate than recently established methods even in the presence of the small number of &lt;small&gt;GCPs&lt;/small&gt;.