Additive Smoothing Research Articles

The relationship of serum klotho levels and triglyceride glucose index-related indicators

BackgroundKlotho, an anti-aging protein, is linked to energy metabolism. There is limited research on the association of serum klotho and triglyceride glucose (TyG) index-related indicators. Our research aims to investigate the relationship of serum klotho with TyG-BMI (body mass index), TyG-WC (waist circumference), and TyG-WHtR (waist-to-height ratio).MethodsFrom 2007 to 2016, we examined 6,370 participants in the National Health and Nutrition Examination Survey (NHANES). The enzyme-linked immunosorbent assay (ELISA) was utilized to measure serum klotho. We calculated the TyG-BMI, TyG-WC, and TyG-WHtR based on fasting triglycerides, fasting glucose, BMI, WC, and WHtR. Multiple linear regression analysis was used to evaluate the association of serum klotho with TyG-BMI, TyG-WC, and TyG-WHtR. Additionally, generalized additive model (GAM) and smoothing curves were used to evaluate the linear and nonlinear relationships. A piecewise regression model was also utilized to test for threshold effects and determine the breakpoints. Finally, the potential independent associations of serum klotho with TyG-BMI, TyG-WC, and TyG-WHtR were further explored using subgroup analysis.ResultsWe observed a statistically significant difference in serum klotho levels across different quartiles of the population. Based on the multiple linear regression analysis, serum klotho levels were negatively associated with TyG-related indicators. There was a nonlinear relationship between the serum klotho and TyG-BMI, TyG-WC, and TyG-WHtR. The segmented regression analysis revealed that the breakpoints of TyG-BMI, TyG-WC, and TyG-WHtR were 5.42, 6.67, and 1.89, respectively. Subgroup analysis showed that TyG-related indicators interacted with gender and diabetes.ConclusionsIn this study, a negative and nonlinear relationship was identified between serum klotho and TyG-related indicators. Further research is needed to clarify the potential mechanisms that may link serum klotho to TyG-BMI, TyG-WC, and TyG-WHtR.

Generalized Additive Spatial Smoothing (GASS): A Multiscale Regression Framework for Modeling Neighborhood Effects Across Spatial Supports

A new technique called generalized additive spatial smoothing (GASS) is introduced for modeling neighborhood effects within a regression framework. GASS has a number of desirable features, namely that it provides a data-driven mechanism for endogenously selecting neighborhoods based on a spatial scale hyperparameter. By allowing different scale hyperparameters to be selected for different relationships in the model, the technique is inherently multiscale and allows neighborhoods to vary by relationship. In addition, GASS includes a measure of uncertainty associated with each scale hyperparameter. These characteristics make it attractive for modeling phenomena where proximity might be an important aspect of a process, especially when a clear definition of proximity is not immediately available. Through multiscale data-driven spatial smoothing, GASS conducts a form of change of support and therefore also facilitates the incorporation of data from diverse sources. Finally, the technique is flexible and can be adapted and expanded with relative ease because it builds on generalized additive modeling. After providing an overview of the methodology, including a modified backfitting algorithm for calibration, a simulation experiment is described and an empirical example modeling bike-share usage is presented. The simulation results indicate that GASS can generally produce reliable results pertaining to both the regression coefficients and scale hyperparameters, and the results from the empirical example demonstrate that the GASS approach provides a better model fit and captures relationships that might otherwise be obfuscated. Overall, these results highlight the potential of the GASS framework and the importance of measuring multiscale neighborhood effects.

A Study of Suitable Forecasting Model for the Export Quantity of Fresh or Chilled Fish in Thailand

The aim of this study was to find a suitable forecasting model for the export quantity of fresh or chilled fish in Thailand using seven statistical methods including Box-Jenkins method, Holt’s exponential smoothing method, Brown’s exponential smoothing method, damped trend exponential smoothing method, simple seasonal exponential smoothing method, Winters’ additive exponential smoothing method, and Winters’ multiplicative exponential smoothing method. The average monthly export quantity from the website of the Office of Agricultural Economics was used for data analysis. The data set used to create the models was the export quantity from January 2011 to December 2020 for 120 months. The data set used to compare the accuracy was the export quantity from January to December 2021 for 12 months using the lowest mean absolute percentage error. The results indicated that the most accurate method was the damped trend exponential smoothing method.

Local and global trend Bayesian exponential smoothing models

This paper describes a family of seasonal and non-seasonal time series models that can be viewed as generalisations of additive and multiplicative exponential smoothing models to model series that grow faster than linear but slower than exponential. Their development is motivated by fast-growing, volatile time series. In particular, our models have a global trend that can smoothly change from additive to multiplicative and is combined with a linear local trend. Seasonality, when used, is multiplicative in our models, and the error is always additive but heteroscedastic and can grow through a parameter sigma. We leverage state-of-the-art Bayesian fitting techniques to fit these models accurately, which are more complex and flexible than standard exponential smoothing models. When applied to the M3 competition data set, our models outperform the best algorithms in the competition and other benchmarks, thus achieving, to the best of our knowledge, the best results of per-series univariate methods on this dataset in the literature. An open-source software package of our method is available.

Forecasting Non-Oil and Gas Exports in Indonesia Using Double and Triple Exponential Smoothing Methods

Non-oil and gas exports could be forecasted using exponential smoothing for future periods. This study examines non-oil and gas export data in Indonesia from January 2015 to May 2021, indicating trends and seasonality. Based on the data characteristics, the obtained data were analyzed using Holt's double exponential smoothing method and triple exponential smoothing with multiplicative and additives Holt-Winters. The MAPE for all three models is less than 10%, indicating that the method is very good and could be used to forecast the next period. Using MAPE as a comparison, the best model for non-oil and gas exports is the additive Holt-Winters method triple exponential smoothing, which has the lowest MAPE of any model. The best method was employed to forecast data, making it possible for us to anticipate the pattern of non-oil and gas exports. This forecast data could be used as the basis for policymakers' decision-making. The forecast results using this method indicate that the value of non-oil exports will increase for the next period.

A Relative Information Gain-based Query Performance Prediction Framework with Generated Query Variants

Query performance prediction (QPP) methods, which aim to predict the performance of a query, often rely on evidences in the form of different characteristic patterns in the distribution of Retrieval Status Values (RSVs). However, for neural IR models, it is usually observed that the RSVs are often less reliable for QPP because they are bounded within short intervals, different from the situation for statistical models. To address this limitation, we propose a model-agnostic QPP framework that gathers additional evidences by leveraging information from the characteristic patterns of RSV distributions computed over a set of automatically generated query variants, relative to that of the current query. Specifically, the idea behind our proposed method—Weighted Relative Information Gain (WRIG), is that a substantial relative decrease or increase in the standard deviation of the RSVs of the query variants is likely to be a relative indicator of how easy or difficult the original query is. To cater for the absence of human-annotated query variants in real-world scenarios, we further propose an automatic query variant generation method. This can produce variants in a controlled manner by substituting terms from the original query with new ones sampled from a weighted distribution, constructed either via a relevance model or with the help of an embedded representation of query terms. Our experiments on the TREC-Robust, ClueWeb09B, and MS MARCO datasets show that WRIG, by the use of this relative changes in QPP estimate, leads to significantly better results than a state-of-the-art baseline method that leverages information from (manually created) query variants by the application of additive smoothing [ 64 ]. The results also show that our approach can improve the QPP effectiveness of neural retrieval approaches in particular.

Fast and Numerically Stable Particle-Based Online Additive Smoothing: The AdaSmooth Algorithm

We present a novel sequential Monte Carlo approach to online smoothing of additive functionals in a very general class of path-space models. Hitherto, the solutions proposed in the literature suffer from either long-term numerical instability due to particle-path degeneracy or, in the case that degeneracy is remedied by particle approximation of the so-called backward kernel, high computational demands. In order to balance optimally computational speed against numerical stability, we propose to furnish a (fast) naive particle smoother, propagating recursively a sample of particles and associated smoothing statistics, with an adaptive backward-sampling-based updating rule which allows the number of (costly) backward samples to be kept at a minimum. This yields a new, function-specific additive smoothing algorithm, AdaSmooth, which is computationally fast, numerically stable and easy to implement. The algorithm is provided with rigorous theoretical results guaranteeing its consistency, asymptotic normality and long-term stability as well as numerical results demonstrating empirically the clear superiority of AdaSmooth to existing algorithms. Supplementary materials for this article are available online.

Efficient Accreditation Document Classification Using Naïve Bayes Classifier

Objectives: To develop a desktop application that automatically classifies a document as to which area of accreditation documents it should belong to. Specifically, it aims to: a) To create a predictive model that addresses document classification tasks. b) To design and develop an application that classifies documents according to document classification. c) To evaluate the performance measures of the automatic document classification. Methods: We introduce an innovative approach for the automatic classification of accreditation documents. Specifically, an approach of including scanned or captured documents in classification task using Optical Character Recognition (OCR); use TFIDF (Term-frequency Inverse Document Frequency) with stopwords removal, ngram of 1-2 in preprocessing of the text documents; and Naive Bayes algorithm with additive (Laplace/Lidstone) smoothing as a classifier in building our model. Results: Performance measures such as accuracy, precision, recall, and f-score were conducted to evaluate the efficiency of the study. The results showed 82% accuracy, 84% precision, 82% recall, and 82% F-1 score. As we explore the use of OCR for text extraction, TF-IDF for text preprocessing, and Naive Bayes classifier, the results indicate that the proposed approach is efficient. Conclusions: Classification of input documents in whatever forms, may it be captured image, scanned or simple text documents were obtained using OCR, TF-IDF, and Naive Bayes classifier. It provides an efficient way of automatic classification of accreditation documents and it gives an avenue to address limiting factors of the previous works, i.e classifying documents based on one’s opinion and time-consuming classification. Keywords: Accreditation Document Classification; Document Classification Objective Evaluation; TF-IDF; Term frequency-inverse document frequency; Multinomial Naive Bayes; OCR; Optical Character Recognition

Additively manufactured nasopharyngeal swabs for the use in Covid-19 testing

A new methodology to manufacture mucosal membrane contact Covid-19 diagnostic items using powder-bed Additive Manufacturing (AM) and vapor smoothing methods was developed and introduced to fulfill the population testing demand. The technique can rapidly manufacture oral/respiratory diagnostic items at the point of use and at scale. Surface medical device tests were performed to evaluate biocompatibility of such Additively Manufactured Covid-19 diagnostic items. The presented results are the most in-depth medical study of vapor smoothed Additively Manufactured medical articles to date.

Hierarchical multigrid approaches for the finite cell method on uniform and multi-level [formula omitted]-refined grids

This contribution presents a hierarchical multigrid approach for the solution of large-scale finite cell problems on both uniform grids and multi-level hp-discretizations. The proposed scheme takes advantage of the hierarchical basis functions utilized in the finite cell method and the multi-level hp-method, which is attributed to the use of high-order integrated Legendre basis functions and overlay meshes, to yield a simple and elegant multigrid scheme. This simplicity is reflected in the fact that transitioning between multigrid levels only involves the inclusion or exclusion of specific basis functions. All restriction and prolongation operators, therefore, reduce to binary matrices that do not need to be explicitly assembled or applied, saving computational time and memory. Elementwise and patchwise additive Schwarz smoothing techniques are used to mitigate the influence of the cut cells on the conditioning of the linear systems, while maintaining the parallelizability of the solver. The effectiveness of the scheme is numerically verified in various examples and convergence rates that are independent of the cut configuration, mesh size, refinement level and, in certain scenarios, even the polynomial order are shown. A series of numerical examples demonstrate the applicability of the scheme for solving large immersed systems with multiple millions and even billions of unknowns on massively parallel machines.

The Impact of COVID-19 on First Nations People Health Assessments in Australia.

The COVID-19 (coronavirus disease 2019) pandemic has the potential to worsen existing health inequalities faced by Aboriginal and Torres Strait Islander peoples in Australia. We aimed to assess the impact of the pandemic on First Nations people health assessments using an interrupted time series model utilizing data extracted from the Australian Medicare Benefits Schedule database. Additive triple exponential smoothing was used to model health assessments undertaken between January 2017 and December 2019. The model was used to predict health assessments between January 2020 and June 2020 with 95% confidence (P < .05). There was no significant difference between observed and predicted First Nations people health assessments in January, February, and June 2020. However, we found a statistically significant decrease in health assessments in March (16.5%), April (23.1%), and May (17.2%) 2020. The proportion of total health assessments delivered via telehealth was 0.5%, 23.6%, 17.6%, and 10.0% for March, April, May, and June 2020, respectively. The decrease in total First Nations people health assessments compounds the risk of poorer health outcomes in this population already vulnerable due to a high burden of chronic disease and considerable social, economic, and health inequalities. Strategies to improve the delivery of telehealth for First Nations people must be considered.

Spectrally Sparse Nonparametric Regression via Elastic Net Regularized Smoothers

Nonparametric regression frameworks, such as generalized additive models (GAMs) and smoothing spline analysis of variance (SSANOVA) models, extend the generalized linear model (GLM) by allowing for unknown functional relationships between an exponential family response variable and a collection of predictor variables. The unknown functional relationships are typically estimated using penalized likelihood estimation, which adds a roughness penalty to the (negative) log-likelihood function. In this article, I propose a spectral parameterization of a smoothing spline, which allows for an efficient application of Elastic Net regression to smooth and select eigenvectors of a kernel matrix. The classic (ridge regression) solution for a smoothing spline is a special case of the proposed kernel eigenvector smoothing and selection operator. Extensions for tensor product smoothers are developed for both the GAM and SSANOVA frameworks. Using simulated and real data examples, I demonstrate that the proposed approach offers practical and computational gains over typical approaches for fitting GAMs, SSANOVA models, and Elastic Net penalized GLMs. Supplementary materials for this article are available online.

Plasma additive layer manufacture smoothing (PALMS) technology – An industrial prototype machine development and a comparative study on both additive manufactured and conventional machined AISI 316 stainless steel

Additive Layer Manufacturing (ALM) of metals is rapidly changing the landscape of industrial manufacturing. Its deployment is however still hindered by extremely rough native surfaces, and drastic difficulties in efficiently applying conventional finishing methods. This paper presents the PALMS process, derived from electrolytic plasma polishing, as a solution to this problem. The viability of the process on a scale compatible with commercial use is demonstrated with a prototype industrial implementation. PALMS was applied on AISI 316 stainless steel pieces produced either by ALM or by conventional machining (CM.) Surface states, microstructures and other properties were compared pre- and post-PALMS. Significant improvements in surface state were observed after a 10 min treatment, with a 5-fold reduction in roughness. ALM surfaces were not affected negatively by PALMS in any way measured, and showed slight improvements in hardness and pore density. Two PVD coatings (TiN and WCC) were finally applied Post-PALMS, to test the compatibility of the process with further industrially relevant surface treatments. PALMS enables good coating adhesion on ALM pieces, with improved friction and wear properties compared to their CM counterparts.

Additive models with trend filtering

We study additive models built with trend filtering, that is, additive models whose components are each regularized by the (discrete) total variation of their $k$th (discrete) derivative, for a chosen integer $k\geq0$. This results in $k$th degree piecewise polynomial components, (e.g., $k=0$ gives piecewise constant components, $k=1$ gives piecewise linear, $k=2$ gives piecewise quadratic, etc.). Analogous to its advantages in the univariate case, additive trend filtering has favorable theoretical and computational properties, thanks in large part to the localized nature of the (discrete) total variation regularizer that it uses. On the theory side, we derive fast error rates for additive trend filtering estimates, and show these rates are minimax optimal when the underlying function is additive and has component functions whose derivatives are of bounded variation. We also show that these rates are unattainable by additive smoothing splines (and by additive models built from linear smoothers, in general). On the computational side, we use backfitting, to leverage fast univariate trend filtering solvers; we also describe a new backfitting algorithm whose iterations can be run in parallel, which (as far as we can tell) is the first of its kind. Lastly, we present a number of experiments to examine the empirical performance of trend filtering.

GPU-Based Blind Watermarking Scheme for 3D Multiresolution Meshes Using Unlifted Butterfly Wavelet Transformation

3D mesh watermarking in the transform domain requires significant computational complexity. This is due mainly to the incessant use of high-resolution meshes which require more and more resources. Normally, this is an expensive work that harms the commercial chain of low computational cost applications requiring content protection or enrichment. To tackle this issue, we proposed herein a high-capacity and blind watermarking scheme for 3D multiresolution semi-regular meshes while maintaining a trade-off between efficiency and robustness. For this purpose, our solution uses an unlifted butterfly wavelet transform technique that explores the computing power of the Graphic Processing Units (GPU) architecture and the Open Computing Language (OpenCL) framework. The robustness was optimized by generating a turbo-encoded watermark. This latter is embedded in the wavelet coefficients after their spherical parametrization at various levels of details using the least significant bit technique. The method allows a better imperceptibility of the watermark and invariability to affine transformation. It also shows comparative robustness against most of the geometric attacks including additive noise, quantization, smoothing and compression. Moreover, the comparison with other serial watermarking schemes proves the effectiveness in terms of computational complexity of our method. OpenCL embedding implementation offers 3–9 $$\times $$ speedups with a low-power GPU architecture for different mesh sizes. In case of extraction procedure, the speedups obtained vary between 2 $$\times $$ and 12 $$\times $$.

Forecasting US Tourists’ inflow to Slovenia by modified Holt-Winters Damped model: A case in the Tourism industry logistics and supply chains

Abstract Forecasting is important in many branches of logistics, including the logistics related to Tourism supply chains. With an increasing inflow of American tourists, planning and forecasting the US tourists’ inflow to Slovenia have gained far more importance attention amongst scholars and practitioners. This study, therefore, was conducted to forecast the American tourists’ inflow to Slovenia using one of the predictive models based on the exponential smoothing approach, namely Holt-Winters damped additive (HWDA) exponential smoothing method. The model was modified by several improvements, while the obtained results were generalized to other supply chain components. The results show that the forecasting system can predict well the observed inflow, while the methodology used to derive the model might have enriched the plethora of existing practical forecasting approaches in the tourism domain. Benchmarking demonstrates that the proposed model outperforms a competitive ARIMA model and official forecasts. The practical implications are also discussed in this paper.

A novel case-control subsampling approach for rapid model exploration of large clustered binary data.

In many settings, an analysis goal is the identification of a factor, or set of factors associated with an event or outcome. Often, these associations are then used for inference and prediction. Unfortunately, in the big data era, the model building and exploration phases of analysis can be time-consuming, especially if constrained by computing power (ie, a typical corporate workstation). To speed up this model development, we propose a novel subsampling scheme to enable rapid model exploration of clustered binary data using flexible yet complex model set-ups (GLMMs with additive smoothing splines). By reframing the binary response prospective cohort study into a case-control-type design, and using our knowledge of sampling fractions, we show one can approximate the model estimates as would be calculated from a full cohort analysis. This idea is extended to derive cluster-specific sampling fractions and thereby incorporate cluster variation into an analysis. Importantly, we demonstrate that previously computationally prohibitive analyses can be conducted in a timely manner on a typical workstation. The approach is applied to analysing risk factors associated with adverse reactions relating to blood donation.

Fast symmetric additive covariance smoothing

A fast bivariate smoothing approach for symmetric surfaces is proposed that has a wide range of applications. It is shown how it can be applied to estimate the covariance function in longitudinal data as well as multiple additive covariances in functional data with complex correlation structures. The proposed symmetric smoother can handle (possibly noisy) data sampled on a common, dense grid as well as irregularly or sparsely sampled data. Estimation is based on bivariate penalized spline smoothing using a mixed model representation and the symmetry is used to reduce computation time compared to the usual non-symmetric smoothers. The application of the approach in functional principal component analysis for very general functional linear mixed models is outlined and its practical value is demonstrated in two applications. The approach is evaluated in extensive simulations. Documented open source software is provided that implements the fast symmetric bivariate smoother building on established algorithms for additivemodels.

Cropping-resilient 3D mesh watermarking based on consistent segmentation and mesh steganalysis

This paper presents a new approach to 3D mesh watermarking using consistent segmentation and mesh steganalysis. The method is blind, statistical, and highly robust to cropping attack. The primary watermarking domain is calculated by shape diameter function and the outliers of segments are eliminated by computing the consistency interval of vertex norms. In the watermark embedding process, the mesh is divided into several segments and the same watermark is inserted in each segment. In the watermark extraction process, the final watermark among watermark candidates extracted from multiple segments is determined through watermark trace analysis that is kind of mesh steganalysis. We analyze the watermark trace energy of multiple segments of a mesh and detect the final watermark in the segment with the highest watermark trace energy. To analyze the watermark trace energy, we employ nonlinear least-squares fitting. The experimental results show that the proposed method not only achieves significantly high robustness against cropping attack, but also resists common signal processing attacks such as additive noise, quantization, smoothing and simplification.

Investigation of additive manufacturing surface smoothing process

PurposeThe additive manufacturing technologies have been facing an extraordinary growth along the past years. This phenomenon might be correlated with rise of low-cost FDM technologies into the non-professional market segment. In contrast with that, among the main disadvantages found in this sort of equipment are the final object finishing and low mechanical strength. For that reason, the purpose of this paper is to present and characterise a surface treatment which is based on solvent vapour attack and that is also known as smoothing process. In addition, a concise overview about the theory beneath this process is presented besides an experimental study that evaluates the main effects on the mechanical properties of object.Design/methodology/approachTo analyse the benefits of this process, the authors preliminarily investigated the working mechanism that supports such surface treatment. It allowed them to identify and select a proper solvent for each material. The authors have also established that the exposure time repetition numbers (passes) were the main variables, whereas temperature, solvent type, drying time, object direction and object shape were constants. The main object dimensions, surface roughness, absorbed solvent mass and mechanical strength were the main study responses.FindingsAs a result of this work, the peak-peak roughness was reduced in 71 per cent, indicating the potential benefit of this process. On the other hand, excessive solvent exposure implied on relevant dimensional distortions and internal disruptures. It was also possible to see that the vapourised solvent penetrate into the object surface and fused layers and filaments. As consequence, the mechanical strength was also improved.Originality/valueDespite the growth that additive manufacturing market segment has seen along the past years, the finishing and mechanical strength of low-cost equipment still lack for improvements. For that reason, applications like solvent vapour attack or smoothing process new perspectives for this non-professional segment, whereas roughness and mechanical strength are improved after its treatment. As a consequence, it is possible to consider a final object to be obtained directly from low-cost FDM in combination with smoothing process.