Flexible Extension Research Articles

Inference for two Weibull populations under joint generalized progressive type-I hybrid censoring with a simulation study and applications

The generalized progressive censoring scheme has been considered one of the most general cases of censoring schemes. In this study, we consider two Weibull populations under a jointly generalized progressive hybrid censoring scheme as a more flexible extension of the exponential distribution. The methods presented in this paper let experimenters evaluate life testing studies in the case of the most generalized censoring scheme based on a flexible distribution that has increasing, constant, and decreasing failure rates. The maximum likelihood method is used to obtain point estimates of the unknown parameters and the corresponding approximate confidence intervals by using asymptotic theory and bootstrap sampling. The Bayesian inferences are handled under informative and non-informative priors. The highest posterior density credible intervals are also obtained for the Bayesian estimations. We further obtained results with a challenging task an optimal censoring scheme using the A-optimality, D-optimality, and F-optimality criterion to let researchers determine the optimal censoring plan before conducting experiments or collecting data. Following the numerical results within this paper, A-optimality and D-optimality proposed the same scheme, while F-optimality proposed a scheme similar to them. In the last part of the study, we provide simulation studies under different censoring plans and use a numerical example to exemplify the theoretical outcomes. It is observed that the best estimation performances are obtained by informative Bayesian methods.

Flexible Constant-Power Range Extension of Self-Oscillating System for Wireless In-Flight Charging of Drones

Flexible Constant-Power Range Extension of Self-Oscillating System for Wireless In-Flight Charging of Drones

A New Flexible Extension of the XLindley Distribution with Properties and Application on Income Tax and Carbon Fibers Data

A new and enhanced extension of the XLindley distribution termed the sized-biased XLindley distribution (SBXLD), has been introduced. This new model explores two specific variants: the length-biased XLindley distribution and the area-biased XLindley distribution. Various crucial properties such as moments, moment generating function, quantile function, survival, and hazard functions, mean residual life function, and Rényi entropy have been derived and extensively investigated. For parameter estimation, five distinct methods have been employed to estimate the model parameters. Through a comprehensive simulation study, the most effective estimation method has been identified. The applicability and efficiency of the SBXLD model have been demonstrated using two datasets from different domains. It has been observed that the SBXLD model effectively analyzed these datasets and yielded superior results compared to other competitive distributions under consideration.

The VIVID trial 12-month outcomes of the venous stent for the iliofemoral vein using the Duo venous stent system

ObjectiveThere are varying anatomical and mechanical demands of stent placement in the iliofemoral venous segment; the Duo Venous Stent System is designed to address these issues. The purpose of the VIVID (Venous stent for the Iliofemoral Vein Investigational clinical trial using the Duo Venous Stent System) study investigated the safety and efficacy of the Duo Venous Stent System for the treatment of patients with nonmalignant iliofemoral venous obstructive disease. MethodsThis was an international, prospective, multicenter, single-arm study that enrolled patients with symptomatic nonthrombotic (NT), post-thrombotic syndrome (PTS), or acute deep vein thrombotic (aDVT) iliofemoral venous outflow obstruction. The primary safety end point was freedom from major adverse events at 30 days after the index procedure. The primary efficacy end point was primary patency of stent-bearing segments at 12 months. Secondary and observational end points included symptom relief, primary-assisted patency, secondary patency, and device success. Patients remain in follow-up for 36 months. ResultsA total of 162 patients were enrolled at 30 sites in the United States and Poland. The primary safety end point was achieved in 98.7% of patients against a predefined performance goal of 89.0% (P < .0001). The primary safety end point was achieved in 100%, 95.0%, and 100% of the NT, PTS, and aDVT cohorts, respectively. The primary efficacy end point was met in 90.2% compared with the performance goal of 77.3% (P = .0002). Primary patency was observed in 95.2% of patients with NT disease, 79.4% of those with PTS, and 86.7% of those with aDVT. No stent fracture, migration, or embolization occurred through 12 months. Patient-reported outcomes showed improvements in Venous Clinical Severity Score, Villalta, EQ-5D-3 L, and VEINES-QoL/Sym scores from baseline through 6 and 12 months. ConclusionsThrough 12 months, the Duo Venous Stent System is safe and effective for the treatment of nonmalignant iliofemoral venous obstructive disease. Clinical RelevanceThe VIVID (Venous stent for the Iliofemoral Vein Investigational clinical trial using the Duo Venous Stent System) investigational device exemption is the first study of a hybrid, venous stent specifically designed to address the anatomic challenges of the iliofemoral venous system. The Duo Venous Stent System consists of a self-expanding nitinol Hybrid Stent used independently or in conjunction with the flexible extension Duo Extend Stent. This is the first report of primary investigational device exemption VIVID Study, assessing the safety and efficacy of the Duo Venous Stent System to treat patients with nonmalignant, symptomatic iliofemoral venous outflow obstruction. The Duo Venous Stent System successfully met its 12-month safety and effectiveness performance goals.

A novel asymmetric extension of power XLindley distribution: properties, inference and applications to engineering data

It is impossible to overstate the importance of using trigonometric functions appropriately in distribution theory. The main contribution of the research is to construct a flexible trigonometric extension of the power XLindley distribution. More specifically, we build an innovative two-parameter lifetime distribution known as the sine power XLindley distribution (SPXLD) using characteristics from the sine-generated family of distributions. As the main motivational fact, it provides an attractive alternative to the power Lindley, power XLindley, weighted Lindley, and extended power Lindley distributions; it may be better able to model lifetime phenomena presenting data of leptokurtic and platkurtic nature. In contrast to the increasing, decreasing, and reversed-j-shaped hazard rate function, the density exhibits asymmetric shapes with varying peakedness levels. Several significant characteristics are illustrated, including moments, the quantile function, the probability density function in series representation, the stress-strength reliability, and incomplete moments. To analyze the behavior of the suggested distribution, sixteen estimation techniques are applied, such as the maximum likelihood, percentiles, some methods of minimum distances, some methods based on minimum and maximum spacing distances, and the Kolmogorov method. After that, an extensive simulation study and the examination of two survival real datasets are used to show the viability, usefulness, and adaptability of the SPXLD. Relevant goodness of fit criteria demonstrates that the SPXLD fits several current distributions.

Distributional modelling of positively skewed data via the flexible Weibull extension distribution

SummaryThe time until an event occurs is often known to have a skewed distribution. To model this, a statistical distribution called the two‐parameter flexible Weibull extension (FWE) has been proposed. In this paper, the FWE distribution is used to model datasets through the use of generalised additive models for location, scale and shape (GAMLSS) distributional regression. GAMLSS is the only regression technique that can examine the effects of both categorical and numeric predictors on all the parameters of the distribution used to fit the dependent variable. To make it easier to use the FWE distribution through GAMLSS, the RelDists R package is proposed. A simulation study shows that FWE modelling through GAMLSS provides reliable parameter estimates even in the presence of factors that affect the distribution.

Negative cumulative extropy of uncertain variables with applications

Recently, a measure of uncertainty termed by negative cumulative extropy (NCEX) is proposed by Tahmasebi and Toomaj (2022). It is a flexible extension of extropy measure which was proposed by Lad, Sanfilippo, and Agro (2015). To measure the uncertainty of uncertain variables, in this article, we propose the concept of NCEX through uncertainty distributions. Some properties of NCEX are derived, and some practical examples of uncertain variables are given. Several basic theorems are proposed. A formula of cross-extropy is defined via inverse uncertainty distributions. We also delve into a pattern recognition problem to highlight the importance of cross-extropy. Finally, applications to portfolio selection and signal processing are presented.

Direct electromagnetic information processing with planar diffractive neural network.

Diffractive neural network in electromagnetic wave-driven system has attracted great attention due to its ultrahigh parallel computing capability and energy efficiency. However, recent neural networks based on the diffractive framework still face the bottlenecks of misalignment and relatively large size limiting their further applications. Here, we propose a planar diffractive neural network (pla-NN) with a highly integrated and conformal architecture to achieve direct signal processing in the microwave frequency. On the basis of printed circuit fabrication process, the misalignment could be effectively circumvented while enabling flexible extension for multiple conformal and stacking designs. We first conduct validation on the fashion-MNIST dataset and experimentally build up a system using the proposed network architecture for direct recognition of different geometry structures in the electromagnetic space. We envision that the presented architecture, once combined with the advanced dynamic maneuvering techniques and flexible topology, would exhibit unlimited potentials in the areas of high-performance computing, wireless sensing, and flexible wearable electronics.

The Life Happens Pass: Use of a Flexible and Fair Assignment Extension Policy

Students sometimes struggle with life events that interfere with the timely completion of course assessments and assignments. For instructors, equity concerns arise because some students may feel comfortable requesting extensions outside those indicated in the syllabus and university policy, whereas others may not. This Quick Fix article describes an extension policy called the “life happens pass” that provides an opportunity for instructors to balance empathy and fairness. It also describes lessons we learned through its implementation. The policy allows each student to request a one-time, seven-day, “no questions asked” extension for any assessment except the final exam. Across 28 classes, approximately one-third of students used this “life happens to pass”; remarkably, most students kept to the original due date. Furthermore, the average rate of use was consistent across course levels. Together, these results suggest that the policy adds flexibility to assist students when needed while maintaining structure in the course. From the instructor perspective, this policy has helped us fairly support students while decreasing stress associated with decision making about extension requests.

A Gaussian-process approximation to a spatial SIR process using moment closures and emulators.

The dynamics that govern disease spread are hard to model because infections are functions of both the underlying pathogen as well as human or animal behavior. This challenge is increased when modeling how diseases spread between different spatial locations. Many proposed spatial epidemiological models require trade-offs to fit, either by abstracting away theoretical spread dynamics, fitting a deterministic model, or by requiring large computational resources for many simulations. We propose an approach that approximates the complex spatial spread dynamics with a Gaussian process. We first propose a flexible spatial extension to the well-known SIR stochastic process, and then we derive a moment-closure approximation to this stochastic process. This moment-closure approximation yields ordinary differential equationsfor the evolution of the means and covariances of the susceptibles and infectious through time. Because these ODEs are a bottleneck to fitting our model by MCMC, we approximate them using a low-rank emulator. This approximation serves as the basis for our hierarchical model for noisy, underreported counts of new infections by spatial location and time. We demonstrate using our model to conduct inference on simulated infections from the underlying, true spatial SIR jump process. We then apply our method to model counts of new Zika infections in Brazil from late 2015 through early 2016.

Fractional Adaptive Resonance Theory (FRA-ART): An Extension for a Stream Clustering Method with Enhanced Data Representation

Clustering data streams has become a hot topic and has been extensively applied to many real-world applications. Compared with traditional clustering, data stream clustering is more challenging. Adaptive Resonance Theory (ART) is a powerful (online) clustering method, it can automatically adjust to learn both abstract and concrete information, and can respond to arbitrarily large non-stationary databases while having fewer parameters, low computational complexity, and less sensitivity to noise, but its limited feature representation hinders its application to complex data streams. In this paper, considering its advantages and disadvantages, we present its flexible extension for stream clustering, called fractional adaptive resonance theory (FRA-ART). FRA-ART enhances data representation by fractionally exponentiating input features using self-interactive basis functions (SIBFs) and incorporating feature interaction through cross-interactive basis functions (CIBFs) at the cost only of introducing an additionally adjustable fractional order. Both SIBFs and CIBFs can be precomputed using existing algorithms, making FRA-ART easily adaptable to any ART variant. Finally, comparative experiments on five data stream datasets, including artificial and real-world datasets, demonstrate FRA-ART’s superior robustness and comparable or improved performance in terms of accuracy, normalized mutual information, rand index, and cluster stability compared to ART and the state-of-the-art G-Stream algorithm.

A modified EM-type algorithm to estimate semi-parametric mixtures of non-parametric regressions

Semi-parametric Gaussian mixtures of non-parametric regressions (SPGMNRs) are a flexible extension of Gaussian mixtures of linear regressions (GMLRs). The model assumes that the component regression functions (CRFs) are non-parametric functions of the covariate(s) whereas the component mixing proportions and variances are constants. Unfortunately, the model cannot be reliably estimated using traditional methods. A local-likelihood approach for estimating the CRFs requires that we maximize a set of local-likelihood functions. Using the Expectation-Maximization (EM) algorithm to separately maximize each local-likelihood function may lead to label-switching. This is because the posterior probabilities calculated at the local E-step are not guaranteed to be aligned. The consequence of this label-switching is wiggly and non-smooth estimates of the CRFs. In this paper, we propose a unified approach to address label-switching and obtain sensible estimates. The proposed approach has two stages. In the first stage, we propose a model-based approach to address the label-switching problem. We first note that each local-likelihood function is a likelihood function of a Gaussian mixture model (GMM). Next, we reformulate the SPGMNRs model as a mixture of these GMMs. Lastly, using a modified version of the Expectation Conditional Maximization (ECM) algorithm, we estimate the mixture of GMMs. In addition, using the mixing weights of the local GMMs, we can automatically choose the local points where local-likelihood estimation takes place. In the second stage, we propose one-step backfitting estimates of the parametric and non-parametric terms. The effectiveness of the proposed approach is demonstrated on simulated data and real data analysis.

Time-Resolved Ion Mobility Mass Spectrometry to Solve Conformational Changes in a Cryptochrome.

Many biological mechanisms rely on the precise control of conformational changes in proteins. Understanding such dynamic processes requires methods for determining structures and their temporal evolution. In this study, we introduce a novel approach to time-resolved ion mobility mass spectrometry. We validated the method on a simple photoreceptor model and applied it to a more complex system, the animal-like cryptochrome from Chlamydomonas reinhardtii (CraCRY), to determine the role of specific amino acids affecting the conformational dynamics as reaction to blue light activation. In our setup, using a high-power LED mounted in the source region of an ion mobility mass spectrometer, we allow a time-resolved evaluation of mass and ion mobility spectra. Cryptochromes like CraCRY are a widespread type of blue light photoreceptors and mediate various light-triggered biological functions upon excitation of their inbuilt flavin chromophore. Another hallmark of cryptochromes is their flexible carboxy-terminal extension (CTE), whose structure and function as well as the details of its interaction with the photolyase homology region are not yet fully understood and differ among different cryptochromes types. Here, we addressed the highly conserved C-terminal domain of CraCRY, to study the effects of single mutations on the structural transition of the C-terminal helix α22 and the attached CTE upon lit-state formation. We show that D321, the putative proton acceptor of the terminal proton-coupled electron transfer event from Y373, is essential for triggering the large-scale conformational changes of helix α22 and the CTE in the lit state, while D323 influences the timing.

Pine Wilt Disease Segmentation with Deep Metric Learning Species Classification for Early-Stage Disease and Potential False Positive Identification

Pine Wilt Disease poses a significant global threat to forests, necessitating swift detection methods. Conventional approaches are resource-intensive but utilizing deep learning on ortho-mapped images obtained from Unmanned Aerial Vehicles offers cost-effective and scalable solutions. This study presents a novel method for Pine Wilt Disease detection and classification using YOLOv8 for segmenting diseased areas, followed by cropping the diseased regions from the original image and applying Deep Metric Learning for classification. We trained a ResNet50 model using semi-hard triplet loss to obtain embeddings, and subsequently trained a Random Forest classifier tasked with identifying tree species and distinguishing false positives. Segmentation was favored over object detection due to its ability to provide pixel-level information, enabling the flexible extension of subsequent bounding boxes. Deep Metric Learning-based classification after segmentation was chosen for its effectiveness in handling visually similar images. The results indicate a mean Intersection over Union of 83.12% for segmentation, with classification accuracies of 98.7% and 90.7% on the validation and test sets, respectively.

Markov switching stereotype logit models for longitudinal ordinal data affected by unobserved heterogeneity in responding behavior

AbstractWhen asked to assess their opinion about attitudes or perceptions on Likert-scale, respondents often endorse the midpoint or extremes of the scale and agree or disagree regardless of the content. These responding behaviors are known in the psychometric literature as middle, extremes, aquiescence and disacquiescence response styles that generally introduce bias in the results. One of the key motivations behind our approach is to account for these attitudes and how they evolve over time. The novelty of our proposal, in the context of longitudinal ordered categorical data, is in considering simultaneously the temporal dynamics of the responses (observable ordinal variables) and unobservable answering behaviors, possibly influenced by response styles, through a Markov switching logit model with two latent components. One component accommodates serial dependence and respondent’s unobserved heterogeneity, the other component determines the responding attitude (due to response styles or not). The dependence of the responses on covariates is modelled by a stereotype logit model with parameters varying according to the two latent components. The stereotype logit model is adopted because it is a flexible extension of the proportional odds logit model that retains the advantage of using a single parameter to describe a regressor effect. In the paper, a new interpretation of the parameters of the stereotype model is given by defining the allocation sets as intervals of values of the linear predictor that identify the most probable response. Unobserved heterogeneity, serial dependence and tendency to response style are modelled through our approach on longitudinal data, collected by the Bank of Italy.

Numerical study on dynamic flow of flexible extension nozzle in rocket motors

The use of extension nozzles in rocket propulsion systems is one of the effective methods to increase the geometric expansion ratio and thrust of the nozzle. As a new type of extension nozzle, the flexible extension nozzle can achieve continuous changes in nozzle expansion ratio. To explore the characteristics of the flow field structure at the “bulge” structure and the step structure in the actual configuration of the flexible extension nozzle, and the impact on the performance of the nozzle, a three-dimensional numerical simulation model of the flexible extension nozzle was first established to study the flow field at the “bulge” structure, and then a two-dimensional unsteady numerical simulation model was established by using the coupling technology of dynamic grid and overlapping grid to study the flow field at the step structure. The results show that during the unfolding of the nozzle surface, a complex flow structure of “expansion fan-mixing layer-shock-vortex” were formed in the step area at the junction of the fixed section and the moving extension section. The specific impulse loss of the 3D model is larger than that of the 2D model due to the consideration of the “bulge” structure. The flow loss of the “bulge” structure and the step structure to the nozzle is 0.73% and 0.65% respectively.

The single and double inverse Pareto–Burr XII distribution: Properties, estimation methods, and real-life data applications

Researchers consistently improve statistical tools to provide a flexible method to fit or predict. The solution needs to be more suitable since the approach is sometimes complex and complicated. Generally, statistical tools are generated from ordinary least squares and wavelet methods. However, the reliability tool provides a good fit comparable to state-of-the-art methods, including the complex posterior setting of hyperparameters. In this article, a flexible extension of the Burr XII model, which is the odd inverse Pareto–Burr XII (OIPBXII) distribution, is studied and investigated using statistical tools such as Markov chain Monte Carlo methods. The mathematical properties of the probability density distribution of OIPBXII are derived, and it shows a behavior shape, decreasing, increasing, J-shaped, reversed-J-shaped, bathtub, upside-down bathtub, decreasing–increasing–decreasing hazard rates, and right-skewed, symmetrical, and concave down densities. The OIPBXII parameters are investigated by using seven classical approaches to estimation. Extensive simulation results are presented to explore the performance of these methods for small and large samples. An application to two real-life sets of data from engineering and medicine is analyzed, showing the flexibility of the OIPBXII distribution as compared to existing Burr XII distributions.

A Weighted Skew-Logistic Distribution with Applications to Environmental Data

Skewness and bimodality properties are frequently observed when analyzing environmental data such as wind speeds, precipitation levels, and ambient temperatures. As an alternative to modeling data exhibiting these properties, we propose a flexible extension of the skew-logistic distribution. The proposal corresponds to a weighted version of the skewed logistic distribution, defined by a parametric weight function that allows shapes with up to three modes for the resulting density. Parameter estimation via the maximum likelihood approach is discussed. Simulation experiments are carried out to evaluate the performance of the estimators. Applications to environmental data illustrating the utility of the proposal are presented.

PAPAYA: A library for performance analysis of SQL-based RDF processing systems

Prescriptive Performance Analysis (PPA) has shown to be more useful than traditional descriptive and diagnostic analyses for making sense of Big Data (BD) frameworks’ performance. In practice, when processing large (RDF) graphs on top of relational BD systems, several design decisions emerge and cannot be decided automatically, e.g., the choice of the schema, the partitioning technique, and the storage formats. PPA, and in particular ranking functions, helps enable actionable insights on performance data, leading practitioners to an easier choice of the best way to deploy BD frameworks, especially for graph processing. However, the amount of experimental work required to implement PPA is still huge. In this paper, we present PAPAYA,11 https://github.com/DataSystemsGroupUT/PAPyA a library for implementing PPA that allows (1) preparing RDF graphs data for a processing pipeline over relational BD systems, (2) enables automatic ranking of the performance in a user-defined solution space of experimental dimensions; (3) allows user-defined flexible extensions in terms of systems to test and ranking methods. We showcase PAPAYA on a set of experiments based on the SparkSQL framework. PAPAYA simplifies the performance analytics of BD systems for processing large (RDF) graphs. We provide PAPAYA as a public open-source library under an MIT license that will be a catalyst for designing new research prescriptive analytical techniques for BD applications.

Integrated modeling of the sports and reliability data: Implications of the probabilistic model and deep learning approaches

In this paper, we consider the evolution of a new probability distribution called the exponential flexible Weibull distribution. The proposed exponential flexible Weibull distribution is obtained by combining the flexible Weibull extension with the exponential T-X strategy. For the exponential flexible Weibull, the estimators of the model parameters are derived mathematically. The appraisal of these parameters is accomplished through a simulation study. The applicability and virtuoso of the exponential flexible Weibull distribution are exemplified via two data sets. Furthermore, we implement the state-of-the-art deep learning algorithms, specifically Artificial Neural Networks and Extreme Gradient Boosting, which are widely employed in real-world applications. A comprehensive comparative analysis is conducted to assess the relative performance of these methodologies.