Smooth Model Research Articles

Embolization of Vessels With Irregular Lumen Using the Coil Packing Technique Between the Amplatzer Vascular Plug II Lobes.

The Amplatzer Vascular Plug(AVP) series enables precise positioning and high migration resistance, allowing embolization in short segments; however, inadequate embolization or recanalization may occasionally occur. We hypothesized that leaks may occur when AVPs are implanted in vessels with irregular lumen due to insufficient adherence to the vessel. This hypothesis was tested by experiments with a vascular model. We employed a coil packing technique between the AVP lobes to embolize internal iliac arteries with an irregular lumen. Saline was injected through the Y-shaped glass tubes of the stenotic and the smooth model (without stenotic lesion), and the amount of leakage was measured when the AVP was deployed. The feasibility and effectiveness of filling coils between the lobes of AVP II were evaluated. A total of 11 cases were retrospectively reviewed using this technique for internal iliac artery embolization prior to endovascular aortic repair. The amount of leakage was significantly higher in the presence of stenotic lesions.Insertion of a 2.2 F microcatheter from the side of the proximal lobe of AVP II and filling of coils was achieved in all 11 cases. Follow-up contrast-enhanced CT showed no recanalization, leakage, or other obvious complications. Coil packing technique around Amplatzer Vascular Plugs could be an effective method and a reliable option for arterial embolization, especially in vessels with irregular lumens.

Trajectory optimization of B-splines interpolation based on dynamic error adjustment

In this paper, we propose a motion planning algorithm based on the five-fold B-spline curve for multi-axis motion control systems with non-smooth trajectories, acceleration, plus acceleration over the limit, etc. We construct a smooth transition model for the micro-line segment G3 of the continuous five-fold B-spline curve. A derivation of the corresponding theoretical equation is given. Using the transition model, we propose an optimization method for dynamic adjustment of motion trajectory errors based on the velocity forward planning constraints by smoothing the transition after the trajectory of the micro-line segment for the forward planning velocity. Finally, the design flow for the forward planning of the velocity is given. Finally, the above algorithm is used to perform simulation experiments for the optimization algorithm. Experimental results show that the proposed optimization improves the accuracy of fitting motion trajectories under the constraints of acceleration and deceleration addition, which validates the proposed algorithm.

Forecasting the Turkish lira Exchange Rates Through Univariate Techniques: Can the Simple Models Outperform the Sophisticated Ones?

Forecasting the Turkish lira Exchange Rates Through Univariate Techniques: Can the Simple Models Outperform the Sophisticated Ones?

Modeling compliant bistable mechanisms: An energy method based on the high-order smooth curvature model

This paper applies an energy method based on the high-order smooth curvature model to address the challenges of kinetostatically modeling the complicated nonlinear post-buckling behavior of inclined compliant beams. The proposed energy method is grounded in the principle of minimum strain energy, implying that the total strain energy is minimized at the equilibrium configuration. In this work, the high-order smooth curvature model is adopted to accurately model the bending strain energy of large deformation beams. Subsequently, the Lagrange multiplier method is employed to ascertain the minimum of strain energy while concurrently determining the corresponding tip loads. Additionally, the deformation shape and maximum stress are determined via the smooth curvature model. The proposed method is introduced for the first time in modeling compliant bistable mechanisms, and it is proven that the method can be used for modeling compliant beams with inclined angles ranging from 0 to 90 degrees. Following the modeling, finite element analysis and experimental tests are conducted to verify the accuracy of the proposed energy method. A comprehensive comparative analysis between proposed method and existing methods is conducted. The comparison results prove that the model is more computationally efficient without compromising modeling accuracy The proposed modeling method can not only be used for modeling compliant bistable mechanisms but also has extendable applications in modeling initially-curved compliant beams, contact-aid design problems, and distributed load problems.The present work not only advances the practical application of the proposed energy method but also lays the foundation for future research in modeling compliant mechanisms.

New semi-analytical method for fast transcranial ultrasonic field simulation

Objective. To optimize and ensure the safety of ultrasound brain therapy, personalized transcranial ultrasound simulations are very useful. They allow to predict the pressure field, depending on the patient skull and probe position. Most transcranial ultrasound simulations are based on numerical methods which have a long computation time and a high memory usage. The goal of this study is to develop a new semi-analytical field computation method that combines realism and computation speed. Approach. Instead of the classic ray tracing, the ultrasonic paths are computed by time of flight minimization. Then the pressure field is computed using the pencil method. This method requires a smooth and homogeneous skull model. The simulation algorithm, so-called SplineBeam, was numerically validated, by comparison with existing solvers, and experimentally validated by comparison with hydrophone measured pressure fields through an ex vivo human skull. Main results. SplineBeam simulated pressure fields were close to the experimentally measured ones, with a focus position difference of the order of the positioning error and a maximum pressure difference lower than 6.02%. In addition, for those configurations, SplineBeam computation time was lower than another simulation software, k-Wave’s, by two orders of magnitude, thanks to its capacity to compute the field only at the focal spot. Significance. These results show the potential of this new method to compute fast and realistic transcranial pressure fields. The combination of this two assets makes it a promising tool for real time transcranial pressure field prediction during ultrasound brain therapy interventions.

Modelling for disability: How does artificial intelligence affect unemployment among people with disability? An empirical analysis of linear and nonlinear effects

There is a growing debate among scholars regarding the impact of artificial intelligence (AI) on the employment opportunities and professional development of people with disability. Although there has been an increasing body of empirical research on the topic, it has generally yielded conflicting findings. This study contributes to the ongoing debate by examining the linear and nonlinear effects of AI on the unemployment of people with disability in 40 countries between 2007 and 2021. Using the system Generalized Methods of Moments and panel smooth transition regression, the main conclusions are as follows. First, AI reduces the unemployment of people with disability in the full sample. Second, upon disaggregating the sample based on income level (high income/non-high income) and gender (men/women), the linear model only detects an inverse correlation between AI and unemployment among people with disability in high-income countries and among men, whereas it does not influence unemployment in non-high-income countries and women. Third, the panel smooth transition regression model suggests that the effects of AI on the unemployment of people with disability and among women are only observed once artificial intelligence interest search exceeds a specific threshold level. The effects of AI in non-high-income economies and among women are not significant in the lower regime, which confirms the nonlinear association between AI and the unemployment rate of people with disability. These findings have important policy implications for facilitating the integration of people with disability into the labor market.

Bilateral regularized optimization model for edge-preserving image smoothing

Edge-preserving image smoothing is vital in the field of image processing and computational photography. The state-of-the-art filters based on optimization models have achieved promising performance. However, most of them fail to consider the spatial support in the regularization term, thus limiting the edge-preserving capabilities. In this paper, inspired by the bilateral filter, which consists of a range kernel and a spatial kernel. we propose to leverage bilateral kernel as a penalty function, and embed it into an optimization model for edge-preserving image smoothing. Furthermore, we propose to incorporate an edge-aware weighted scheme in the data term design, which further improves the edge-preserving capability. The bilateral function is non-convex and can be non-trivial to solve. In this paper, we propose a novel iterative solution based on fixed point iteration, where the main burden in each iteration is a bilateral filtering process. We have conducted extensive experiments to evaluate the proposed filter. Experiment results indicate that our filter benefits a variety of image processing tasks. Moreover, we propose an efficient approximation of the proposed filter, which is able to significantly accelerate the filtering process with neglectable sacrifice of smoothing quality.

A parallel multi-resolution Smoothed Particle Hydrodynamics model with local time stepping

Smoothed Particle Hydrodynamics (SPH) model has exhibited remarkable efficacy in addressing strongly nonlinear large deformation problems. However, the expensive computational cost could limit its application. To address this, we propose a novel parallel multi-resolution SPH model with a local time step strategy. This model integrates the multi-resolution model with a Message Passing Interface-based parallelization framework, wherein subdomains discretize the computational domain at varying resolutions. Meanwhile, a local fourth-order Runge-Kutta time integration scheme is introduced, enabling different subdomains to use different time steps. Subdomains of varying resolutions are devoid of overlapping regions, and changes in particle resolution at interfaces are achieved through a dynamic strategy involving particle merging and splitting. Besides that, we conducted a comparative analysis of different SPH discretization formats concerning their impact on the accuracy of multi-resolution particle discretization. Finally, several numerical tests were conducted to validate the accuracy and efficiency of the present multi-resolution SPH model.

Ten years of hand hygiene excellence: a summary of outcomes, and a comparison of indicators, from award-winning hospitals worldwide.

Hand hygiene is a crucial measure for the prevention of healthcare-associated infections (HAIs). The Hand Hygiene Excellence Award (HHEA) is an international programme acknowledging healthcare facilities for their leadership in implementing hand hygiene improvement programmes, including the World Health Organisation's Multimodal Improvement Strategy. This study aimed at summarising the results of the HHEA campaign between 2010 and 2021 and investigating the relationship between different hand hygiene parameters based on data from participating healthcare facilities. A retrospective analysis was performed on datasets from HHEA forms, including data on hand hygiene compliance, alcohol-based handrub (ABHR) consumption, and Hand Hygiene Self-Assessment Framework (HHSAF) scores. Descriptive statistics were reported for each variable. The correlation between variables was inspected through Kendall's test, while possible non-linear relationships between hand hygiene compliance, ABHR consumption and HHSAF scores were sought through the Locally Estimated Scatterplot Smoothing or logistic regression models. A tree-structured partitioning model was developed to further confirm the obtained findings. Ninety-seven healthcare facilities from 28 countries in three world regions (Asia-Pacific, Europe, Latin America) were awarded the HHEA and thus included in the analysis. HHSAF scores indicated an advanced hand hygiene promotion level (median 445 points, IQR 395-480). System change (100 [95-100] points) and institutional safety climate (85 [70-95] points) showed the highest and lowest score, respectively. In most cases, hand hygiene compliance was above 70%, with heterogeneity between countries. ABHR consumption above 20 millilitres per patient-day (ml/PD) was widely reported, with overall increasing trends. HHSAF scores were positively correlated with hand hygiene compliance (τ = 0.211, p = 0.007). We observed a positive correlation between compliance rates and ABHR consumption (τ = 0.193, p < 0.001), although the average predicted consumption was stable around 55-60ml/PD for compliance rates above 80-85%. Logistic regression and partitioning tree analyses revealed that higher HHSAF scores were more likely in the high-ABHR consumption group at cut-offs around 57-59ml/PD. Ten years after its inception, the HHEA proves to be a valuable hand hygiene improvement programme in healthcare facilities worldwide. Consistent results were provided by the different hand hygiene indicators and the HHSAF score represents a valuable proxy measure of hand hygiene compliance.

The Forecast of Streamflow through Göksu Stream Using Machine Learning and Statistical Methods

Forecasting streamflow in stream basin systems plays a crucial role in facilitating effective urban planning to mitigate floods. In addition to employing intricate hydrological modeling systems, machine learning and statistical techniques offer an alternative means for streamflow forecasts. Nonetheless, the precision and dependability of these methods are subjects of paramount importance. This study rigorously investigates the effectiveness of three distinct machine learning techniques and two statistical approaches when applied to streamflow data from the Göksu Stream in the Marmara Region of Turkey, spanning from 1984 to 2022. Through a comparative analysis of these methodologies, this examination aims to contribute innovative advancements to the existing methodologies used in the prediction of streamflow data. The methodologies employed include machine learning methods such as Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Machine (SVM) and statistical methods such as Simple Exponential Smoothing (SES) and Autoregressive Integrated Moving Average (ARIMA) model. In the study, 444 data points between 1984 and 2020 were used as training data, and the remaining data points for the period 2021–2022 were used for streamflow forecasting in the test validation period. The results were evaluated using various metrics, such as the correlation coefficient (r), mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), coefficient of determination (R2), and Nash–Sutcliffe efficiency (NSE). Upon analyzing the results, it was found that the model generated using the XGBoost algorithm outperformed other machine learning and statistical techniques. Consequently, the models implemented in this study demonstrate a high level of accuracy in predicting potential streamflow in the river basin system.

E-PVT: enhanced position-velocity-time scheduler for computer-controlled optical finishing with comprehensive considerations of dynamics constraints, continuity and efficiency

Deterministic computer-controlled optical finishing is an essential approach for achieving high-quality optical surfaces. Its determinism and convergence rely heavily on precise and smooth motion control to guide the machine tool over an optical surface to correct residual errors. One widely supported and smooth motion control model is position-velocity-time (PVT), which employs piecewise cubic polynomials to describe positions. Our prior research introduced a PVT-based velocity scheduling method, demonstrating sub-nanometer level convergence in ion beam figuring (IBF) processes. However, three challenges remained. Firstly, this method relies on quadratic programming, resulting in computational intensiveness for dense tool paths. Secondly, the dynamics constraints and velocity and acceleration continuities are not comprehensively considered, limiting the full potential of PVT-based control. Thirdly, no compensation mechanism existed when dynamics constraints are exceeded. In this study, in response to these challenges, we proposed the Enhanced PVT (E-PVT) method, reducing the time complexity from O(n3) to O(n) while fully addressing dynamics constraints and continuities. A novel compensation method utilizing particle swarm optimization was proposed to address situations where dynamics constraints might be exceeded while maintaining the overall processing efficiency. Validation through simulation and experimentation confirmed the improved performance of E-PVT.

One‐way waveform inversion: Real marine data application

AbstractThe reflection waveform inversion is a powerful technique to build a large‐scale velocity model of the subsurface by fitting the reflected recorded seismic waves. The reflection waveform inversion is designed based on the pillar concept of model and data‐scale separation. Therefore, its success is related to the ability of its forward modelling engine to separate reflected events distinctly from other propagation modes (diving waves, multiples, etc.). However, the standard Born modelling based on the two‐way wave equation may generate internal multiples in case of an insufficient smooth background model. These internal multiples may lead to a distorted sensitivity kernel, which adds more non‐linearity to the inverse problem. In addition, simulating the wave equation using two‐way propagators is still an overburden step of the algorithm especially in large three‐dimensional real surveys. In this proposal, we introduce an alternative to the two‐way wave equation by using a one‐way approach for the reflection waveform inversion. The Born modelling based on one‐way propagators significantly reduces the computational cost and I think it should be allows to relax the smooth background velocity model assumption by restricting the forward modelling to primary reflected waves. After a brief theoretical description of the one‐way waveform inversion, we present an application of the algorithm on the real marine dataset to review its promises and pitfalls. Our approach produces an acceptable large‐scale velocity model whose accuracy is confirmed by the migrated image and the offset gathers.

Pseudospectral methods for continuous-time heterogeneous-agent models

We propose a pseudospectral method to solve heterogeneous-agent models in continuous time. The solution is approximated as a sum of smooth global basis functions, in our case polynomials represented by their values at Chebyshev nodes. We illustrate the method by applying it to a Krusell-Smith model. It solves the differential equations characterizing the steady-state efficiently and precisely, despite using only very few nodes. System dynamics are then automatically differentiated to simulate a linearized model. The full solution takes a third of a second and only uses standard software. A benchmark against finite differences shows that pseudospectral methods achieve far greater precision for a given number of nodes and for a given runtime. We conclude by discussing the methods' applicability, which is promising for smooth multi-dimensional models.

The Role of Smart City in Achieving Sustainable Development: Google Trends Analysis and Exponential Time Series Smoothing Models

The concept of sustainable smart urban development is an important element of the modern discourse on the future development of cities, regions, and the world as a whole, a key component of a strategy aimed at introducing innovations, improving the life quality of citizens, ensuring inclusiveness and sustainability of local development. It integrates the concept of Smart City as the application of advanced innovative technologies to solve urban problems and the concept of Sustainable Development as a balancing of socio-economic and environmental interests to achieve the Sustainable Development Goals. While discussing the problems and prospects introducing any social models or strategies, it is important to understand the level of public interest in this issue, the level of citizens’ awareness, the readiness to receive new information, and the readiness for change. The article is focused on the analysis of retrospective dynamics (from 01.01.2010 to 01.12.2023) and economic and mathematical modelling of forecast trends in changing public interest in the concepts of “Smart City” and “Sustainable Development” (identified using the Google Trends tool). The analysis was carried out for the world as a whole and for Ukraine specifically. The study identifies and describes the evolution of changes in public interest in these topics, identifies the time points of a sharp surge in interest, and conducts a comparative analysis in the content (trends for these two search queries) and spatial context (trends in Ukraine compared to global trends). The countries of the world and regions of Ukraine where user interest in the relevant topics dominates are ranked, and the countries of the world and regions of Ukraine where user interest in the relevant topics dominates are ranked, the TOP-10 are identified. The factors whose influence is relevant to the public interest dynamics are identified (consumer trends, media activity, dynamics of the introduction of relevant concepts, cultural, linguistic, and historical contexts, the general level of education, digital inclusion and consciousness of the population of different countries, etc.) Using the exponential smoothing method, models were built to predict the dynamics of time series – the number of queries from Ukrainian Internet users regarding the concepts of “Smart City” and “Sustainable Development”. Additive forecasting models, which differ in the trend component, are obtained.

Empowering Sustainability: A Consumer-Centric Analysis Based on Advanced Electricity Consumption Predictions

This study addresses the critical challenge of accurately forecasting electricity consumption by utilizing Exponential Smoothing and Seasonal Autoregressive Integrated Moving Average (SARIMA) models. The research aims to enhance the precision of forecasting in the dynamic energy landscape and reveals promising outcomes by employing a robust methodology involving model application to a large amount of consumption data. Exponential Smoothing demonstrates accurate predictions, as evidenced by a low Sum of Squared Errors (SSE) of 0.469. SARIMA, with its seasonal ARIMA structure, outperforms Exponential Smoothing, achieving lower Mean Absolute Percentage Error (MAPE) values on both training (2.21%) and test (2.44%) datasets. This study recommends the adoption of SARIMA models, supported by lower MAPE values, to influence technology adoption and future-proof decision-making. This study highlights the societal implications of informed energy planning, including enhanced sustainability, cost savings, and improved resource allocation for communities and industries. The synthesis of model analysis, technological integration, and consumer-centric approaches marks a significant stride toward a resilient and efficient energy ecosystem. Decision-makers, stakeholders, and researchers may leverage findings for sustainable, adaptive, and consumer-centric energy planning, positioning the sector to address evolving challenges effectively and empowering consumers while maintaining energy efficiency.

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.

Global Prevalence and Mortality of Type-2 Diabetes from 1990 to 2019, with Future Projections to 2023 and 2050: a Systematic Review

Type-2 diabetes (T2DM) is a key public health issue due to its high prevalence and mortality rate. This review estimates future global trends in T2DM prevalence and mortality using the Forecast exponential smoothing (ETS) model. By 2050, estimated prevalence and mortality for all ages are 10.23% and 2.05 million, respectively, and for adults (&gt;20 years) are 11.97% and 1.9 million. Obesity and high BMI are key risk factors. Mortality is higher in adults due to polypharmacy and complications. T2DM prevalence and mortality will continue to rise alarmingly. Urgent international and national measures are needed to address this burden. Collaboration between researchers, clinicians, and policymakers is crucial for optimal health planning and mitigating the impact of T2DM

An improved B-L model for dynamic stall prediction of rough-surface airfoils

The Beddoes-Leishman (B-L) semi-empirical model is a classical method for predicting the dynamic stall of wind turbine airfoils. However, the model performs poorly for rough-surface airfoils due to the premature entry of turbulence and stall condition under the roughness effect. In this study, an improved dynamic stall model is developed for rough-surface airfoils. Two corrections are involved in the modifications. First, the calculation of flow separation points is corrected considering the difference between the normal and tangential separation points, and then an attenuation coefficient is introduced for the effective angle of attack to account for the rough-surface effect. Subsequently, the proposed model is extensively verified by experimental data of S-series airfoils with rough surfaces. Results show that the proposed model can well predict the dynamic stall characteristics of rough-surface airfoils with a higher accuracy than that of the original B-L model under various mean angle of attacks, oscillating amplitudes and reduced frequencies. At last, the model is utilized to predict the operating loads of a wind turbine using the Blade-Element Momentum theory. Significant difference between the dynamic loads predicted by smooth and rough models is observed. This indicates that the roughness effect on the dynamic stall model as well as the prediction of the operating loads on wind turbines cannot be ignored.

Cryptocurrency assets valuation based on statistics model and machine learning

As a matter of fact, cryptocurrency is an exchange media, which uses cryptographic functions to trade, which achieves decentralisation, transparency, and invariance using blockchain. With the rapid development since the first white book proposed in 2008. The market value of cryptocurrency has been exponentially expanded rapidly in recent years. On this basis, programmatic trading strategy are paid more attention by people in trading cryptocurrency contemporarily. With this in mind, this study provides three models, that is, Exponential Smoothing Model (ESM), Autoregressive Integrated Moving Average (ARIMA) model and Long short-term memory (LSTM) model to forecast the future price of cryptocurrency and use sum of square error to analyse the results of these prediction. According to the analysis, LSTM is the best model to forecast the price of cryptocurrency in this article. Overall, machine learning might be more useful to forecast in this fields than traditional statistical models. Investors and researchers in the future could continue to forecast the price of cryptocurrency combined with LSTM model.

Assessment of Persian export pomegranate quality: A reliable non-destructive method based on spectroscopy and chemometrics

The present study investigated the feasibility of visible near-infrared (Vis-NIR) spectroscopy method as a low-cost, quick and reliable method for the prediction of qualitative characteristics (fruit juice percentage, soluble solids, acidity, taste index and vitamin C) of an export variety of Persian pomegranate. For this purpose, pomegranate samples were first analyzed by Vis-NIR spectroscopy, followed by destructive reference methods for quality determination. Subsequently, the data from Vis-NIR were evaluated with different pre-processing methods, among which the combination of the Savitzky–Golay (SG) smoothing model and standard normal variation (SNV) normalization model, generates the best outcomes in predicting six desired qualitative characteristics by partial least square regression (PLSR) model. Fruit juice percentage and pH presented the best prediction models with a correlation coefficient (rP) of 0.98 for both, and root mean square errors of prediction (RMSEP) equal to 0.036 and 0.041, respectively. The values of rp for all desired parameters in this study were higher than 0.95, which pointed to the excellent accuracy of the PLSR models in the prediction of pomegranate qualitative features. It can be concluded from the present study that, Vis-NIR spectroscopy using the PLSR model as a non-destructive functional approach is capable of verifying the quality of Persian export variety pomegranate as an online technique with the aim of grading products before the export phase in order to augment the product marketability.