Negligible Error Research Articles

Image and video compression of fluid flow data

We study the compression of spatial and temporal features in fluid flow data using multimedia compression techniques. The efficacy of spatial compression techniques, including JPEG and JPEG2000 (JP2), and spatiotemporal video compression techniques, namely H.264, H.265, and AV1, in limiting the introduction of compression artifacts and preserving underlying flow physics are considered for laminar periodic wake around a cylinder, two-dimensional turbulence, and turbulent channel flow. These compression techniques significantly compress flow data while maintaining dominant flow features with negligible error. AV1 and H.265 compressions present the best performance across a variety of canonical flow regimes and outperform traditional techniques such as proper orthogonal decomposition in some cases. These image and video compression algorithms are flexible, scalable, and generalizable holding potential for a wide range of applications in fluid dynamics in the context of data storage and transfer.Graphic abstract

Measuring thermal diffusivity when the principal axes are unknown a priori

The thermal diffusivity of polymers may develop anisotropy due material possessing or mechanical loading. The orientation of the anisotropy may not be visually apparent. An analytical study has been performed where the four non-zero components of the diffusivity tensor are estimated for a specimen with in-plane anisotropy. Sensitivity coefficients were calculated for each of the non-zero components of the diffusivity tensor at the four measurement locations. The amplitude of the sensitivity coefficients suggest that the signal received at each sensor is sufficient for estimating the parameters, while the character of the coefficients over time suggests that the parameters are independent. Small values of in-plane anisotropy lead to negligible errors (< 0.4%) in the estimated parameters when the off-diagonal diffusivity is neglected. Larger differences in anisotropy require the estimation of the off-axis terms, which is possible with the addition of a fourth sensor to the extended flash diffusivity method. This additional measurement may be used to verify assumptions of thermally orthotropic behavior in materials.

Model reduction for the material point method via an implicit neural representation of the deformation map

This work proposes a model-reduction approach for the material point method on nonlinear manifolds. Our technique approximates the kinematics by approximating the deformation map using an implicit neural representation that restricts deformation trajectories to reside on a low-dimensional manifold. By explicitly approximating the deformation map, its spatiotemporal gradients—in particular the deformation gradient and the velocity—can be computed via analytical differentiation. In contrast to typical model-reduction techniques that construct a linear or nonlinear manifold to approximate the (finite number of) degrees of freedom characterizing a given spatial discretization, the use of an implicit neural representation enables the proposed method to approximate the continuous deformation map. This allows the kinematic approximation to remain agnostic to the discretization. Consequently, the technique supports dynamic discretizations—including resolution changes—during the course of the online reduced-order-model simulation.To generate dynamics for the generalized coordinates, we propose a family of projection techniques. At each time step, these techniques: (1) Calculate full-space kinematics at quadrature points, (2) Calculate the full-space dynamics for a subset of ‘sample’ material points, and (3) Calculate the reduced-space dynamics by projecting the updated full-space position and velocity onto the low-dimensional manifold and tangent space, respectively. We achieve significant computational speedup via hyper-reduction that ensures all three steps execute on only a small subset of the problem's spatial domain. Large-scale numerical examples with millions of material points illustrate the method's ability to gain an order of magnitude computational-cost saving—indeed real-time simulations—with negligible errors.

PERTANGGUNGJAWABAN PIDANA TERHADAP PENGENDARA YANG MENGAKIBATKAN KEMATIAN PADA KECELAKAAN LALU LINTAS

The development of Indonesia's transportation system is a community need. Given the rather dominant role played by land transport, it is imperative that land transport ensure that traffic is safe, orderly and smooth in order to ensure a smooth flow of various activities in the realization of the public interest. And how to be efficient. Traffic accident frequency. Many of the causes of traffic accidents are caused by negligence and human error, and these traffic accidents have resulted in many losses, such as damage to public facilities and casualties. The research methods used by the authors are prescriptive laws that research library materials or secondary data. The approaches used by the authors are the legal approach and the case approach. A legal approach is taken by directly examining the laws and regulations relevant to the legal issue under investigation. Approaching the case, the author analyzes Court Decision No.665/Pid.B/PN.JKT.PST. Article 311 of the Road Traffic Transport Law No.22 of 2009 precluded the application of Articles 338 and 359 of the Penal Code by the existence of regulations on traffic accident norms and criminal penalties as a special case for traffic accidents. General Criminal Provisions. It is known as a special law that deviates from the general law.

Can we ignore trait-dependent colonization and diversification in island biogeography?

The application of state-dependent speciation and extinction models to phylogenetic trees has shown an important role for traits in diversification. However, this role remains comparatively unexplored on islands, which can include multiple independent clades resulting from different colonization events. To explore whether assuming no dependence on traits leads to bias in inference on island dynamics, we extend an island biodiversity model, DAISIE (Dynamic Assembly of Islands through Speciation, Immigration, and Extinction) to include trait-dependent diversification simulations, and evaluate the robustness of the inference model which ignores this trait-dependence. Our results indicate that when the differences between colonization, extinction, and speciation rates between trait states are moderate, the model shows negligible error for a variety of island diversity metrics, suggesting that island diversity dynamics can be accurately estimated without the need to explicitly model trait dependence. We conclude that for many biologically realistic scenarios with trait-dependent diversification and colonization, this simple trait-less inference model is informative and robust to trait effects on colonization, speciation, and extinction. Nonetheless, our new simulation model may provide a useful tool for studying patterns of trait variation.

Massively parallel universal linear transformations using a wavelength-multiplexed diffractive optical network

We report deep learning-based design of a massively parallel broadband diffractive neural network for all-optically performing a large group of arbitrarily-selected, complex-valued linear transformations between an input and output field-of-view, each with N_i and N_o pixels, respectively. This broadband diffractive processor is composed of N_w wavelength channels, each of which is uniquely assigned to a distinct target transformation. A large set of arbitrarily-selected linear transformations can be individually performed through the same diffractive network at different illumination wavelengths, either simultaneously or sequentially (wavelength scanning). We demonstrate that such a broadband diffractive network, regardless of its material dispersion, can successfully approximate N_w unique complex-valued linear transforms with a negligible error when the number of diffractive neurons (N) in its design matches or exceeds 2 x N_w x N_i x N_o. We further report that the spectral multiplexing capability (N_w) can be increased by increasing N; our numerical analyses confirm these conclusions for N_w > 180, which can be further increased to e.g., ~2000 depending on the upper bound of the approximation error. Massively parallel, wavelength-multiplexed diffractive networks will be useful for designing high-throughput intelligent machine vision systems and hyperspectral processors that can perform statistical inference and analyze objects/scenes with unique spectral properties.

Mesoscale Analysis of Rubber Particle Effect on Indirect Tensile and Flexural Tensile Strength of Crumb Rubber Mortar

This paper presents a mesoscale model to study the influence of rubber particles on the mechanical performance of crumb rubber mortar (CRM). The indirect tensile and flexural behaviors of CRM with different rubber replacement rates, shapes, and sizes were investigated. Rubber mortar is assumed to be a three-phase material composed of rubber aggregate, a mortar matrix, and an interface transition zone (ITZ). Numerical analysis showed that rubber content was the governing factor affecting the reduction rate of indirect tensile and flexural strength. The effect of the ITZ on the tensile strength of CRM was within one percent, which could be ignored. The influence of rubber particle size was investigated by analyzing CRM models containing five different rubber sizes from 0.86 mm to 7 mm. For each size, six different models with randomly distributed rubber particles were set up. CRM models presented a similar average strength even with different rubber particle sizes. However, the strength variation among the random models became higher when the rubber particle size increased. Numerical results also proved that treating rubber particles as pores in modeling led to negligible errors. Then, a prediction formula after considering the increase in air content is provided. Finally, the accuracy of numerical simulations was verified through a series of experimental studies.

A Deterministic Adjoint-Based Semi-Analytical Algorithm for Fast Response Change Computations in Proton Therapy

In this paper we propose a solution to the need for a fast particle transport algorithm in Online Adaptive Proton Therapy capable of cheaply, but accurately computing the changes in patient dose metrics as a result of changes in the system parameters. We obtain the proton phase-space density through the product of the numerical solution to the one-dimensional Fokker-Planck equation and the analytical solution to the Fermi-Eyges equation. Moreover, a corresponding adjoint system was derived and solved for the adjoint flux. The proton phase-space density together with the adjoint flux and the metric (chosen as the energy deposited by the beam in a variable region of interest) allowed assessing the accuracy of our algorithm to different perturbation ranges in the system parameters and regions of interest. The algorithm achieved negligible errors () for small Hounsfield unit (HU) perturbation ranges (–40 HU to 40 HU) and small to moderate errors (3% to 17%) – in line with the well-known limitation of adjoint approaches – for large perturbation ranges (–400 HU to 400 HU) in the case of most clinical interest where the region of interest surrounds the Bragg peak. Given these results coupled with the capability of further improving the timing performance it can be concluded that our algorithm presents a viable solution for the specific purpose of Online Adaptive Proton Therapy.

Numerical Computation of SEIR Model for the Zika Virus Spreading

The purpose of this study is to present the numerical performances and interpretations of the SEIR nonlinear system based on the Zika virus spreading by using the stochastic neural networks based intelligent computing solver. The epidemic form of the nonlinear system represents the four dynamics of the patients, susceptible patients S(y), exposed patients hospitalized in hospital E(y), infected patients I(y), and recovered patients R(y), i.e., SEIR model. The computing numerical outcomes and performances of the system are examined by using the artificial neural networks (ANNs) and the scaled conjugate gradient (SCG) for the training of the networks, i.e., ANNs-SCG. The correctness of the ANNs-SCG scheme is observed by comparing the proposed and reference solutions for three cases of the SEIR model to solve the nonlinear system based on the Zika virus spreading dynamics through the knacks of ANNs-SCG procedure based on exhaustive experimentations. The outcomes of the ANNs-SCG algorithm are found consistently in good agreement with standard numerical solutions with negligible errors. Moreover, the procedure’s constancy, dependability, and exactness are perceived by using the values of state transitions, error histogram measures, correlation, and regression analysis.

Mesoscale analysis of rubber particle effect on compressive strength of crumb rubber mortar

This research proposes a mesoscale model to investigate the effect of rubber particles on the mechanical behaviours of crumb rubber mortar (CRM). In this paper, CRM was viewed as a three-phase material consisting of rubber particles, mortar matrix, and interface transition zone (ITZ). Numerical simulations showed that the strength reduction was mainly affected by the rubber content. For CRM samples containing the same content of rubber but different particle distributions, the average compressive strength of the CRM model was similar, but with slightly different strength deviations. The numerical analysis also showed that idealizing the rubber particles as pores caused negligible errors in the compressive strength of the CRM. A prediction formula that could consider the increase in air content was proposed and used to calculate the simulation results of CRM, which deviated from the experimental values within an acceptable range.

Monte Carlo simulation in risk assessment in mathematical generation of long data

The report will address the main problem in risk measurement, namely the lack of a sufficiently long series of data for the various variables, so that the trend of their development can be formed with negligible error. A model will be made through a mathematical calculation in order to derive a long enough series with statistics reflecting the correlation between the individual variable indices and the standard deviation (volatility) to be able to obtain results with greater accuracy.

Artificial intelligence methods for classification and prediction of potatoes harvested from fertilized soil based on a sensor array response

Diet is one of the biggest vital reasons affecting human life, but the market is abundant with agricultural products treated with pesticides and fertilizers, which puts human health at risk. In this paper, the objective is to discriminate and predict the nature of potatoes (treated traditionally or with chemical fertilizers), one of the most consumed vegetables. To that aim, Potato samples were prepared and their headspace was analyzed. The data was collected by a data acquisition card from a sensor array manufactured with five commercial metal oxide (MOX) gas sensors from potatoes treated with NPK fertilizers, and from those treated traditionally with manure from domestic sheep and donkeys. The responses of the sensors were used to assess the accuracy of the classification and the prediction. Firstly, the k-nearest neighbors (KNN) with cross-validation of 5 folds were used for the classification and the accuracy was 90%. Secondly, the nonlinear autoregressive with exogenous input (NARX) network was used for the prediction, and a correlation rate of 0.99 with a negligible mean square error. From the case studied, our tool is able to identify and predict whether the nature of the potato is treated with NPK fertilizer or with manure from domestic sheep and donkeys with very good rates.

Fast electromigration stress analysis using Low-Rank Balanced Truncation for general interconnect and power grid structures

Electromigration (EM) has become one of the most significant challenges considering longterm reliability in integrated circuit design. The problem is caused by the large current density in circuit interconnections. However, in most cases, we are interested in the EM stress at specific points of the interconnect, such as vias, junctions and boundaries. As a result, Model Order Reduction (MOR) techniques can provide attractive methodologies to reduce the complexity of the original systems. System-theoretic techniques like Balanced Truncation (BT) offer very reliable bounds for the approximation error, compared to moment-matching methods. In this paper, we apply a computationally efficient Low-Rank BT procedure based on the extended Krylov subspace method, that can handle large-scale models and significantly accelerate the EM stress analysis for any general interconnect or power grid structure. For the experimental evaluation, we first consider a general structure interconnect tree to demonstrate the generalizability of the proposed method and then we employ our method on the industrial IBM power grid benchmarks where the its scalability is shown. For the latter, we demonstrate that our method can achieve a speedup up to 238× over a standard transient analysis method and a speedup up to 15× over COMSOL, while exhibiting negligible error.

Application of Optimal Interpolation to Spatially and Temporally Sparse Observations of Aerosol Optical Depth

Aerosol optical depth (AOD) is one of the basic characteristics of atmospheric aerosol. A global ground-based network of sun and sky photometers, the Aerosol Robotic Network (AERONET) provides AOD data with low uncertainty. However, AERONET observations are sparse in space and time. To improve data density, we merged AERONET observations with a GEOS-Chem chemical transport model prediction using an optimal interpolation (OI) method. According to OI, we estimated AOD as a linear combination of observational data and a model forecast, with weighting coefficients chosen to minimize a mean-square error in the calculation, assuming a negligible error of AERONET AOD observations. To obtain weight coefficients, we used correlations between model errors in different grid points. In contrast with classical OI, where only spatial correlations are considered, we developed the spatial-temporal optimal interpolation (STOI) technique for atmospheric applications with the use of spatial and temporal correlation functions. Using STOI, we obtained estimates of the daily mean AOD distribution over Europe. To validate the results, we compared daily mean AOD estimated by STOI with independent AERONET observations for two months and three sites. Compared with the GEOS-Chem model results, the averaged reduction of the root-mean-square error of the AOD estimate based on the STOI method is about 25%. The study shows that STOI provides a significant improvement in AOD estimates.

Concordant Mode Approach for Molecular Vibrations.

The Concordant Mode Approach (CMA) is advanced as a novel hierarchy for increasing the system size and level of theory feasible for quantum chemical computations of harmonic vibrational frequencies. The key concept behind CMA is that transferrable, internal-coordinate normal modes computed at an appropriate lower level of theory (B) comprise a superb basis for converging to vibrational frequencies given by a higher level of theory (A). Accordingly, high-level harmonic frequencies can be evaluated via CMA from a collection of single-point energies that essentially scales linearly in the number of atoms, providing nearly order-of-magnitude CPU time speedups. The accuracy of CMA methods was established by comprehensive tests on over 120 molecules for target Level A = CCSD(T)/cc-pVTZ with auxiliary Level B choices of both CCSD(T)/cc-pVDZ and B3LYP/6-31G(2df,p). Remarkably, the frequency residuals given by the diagonal CMA-0A(nc) scheme exhibit mean absolute deviations (MADs) of only 0.2 cm-1 and standard deviations less than 0.5 cm-1; the corresponding zero-point vibrational energies (ZPVEs) have negligible errors in the vicinity of 0.3 cm-1.

Intelligent predictive stochastic computing for nonlinear differential delay computer virus model

In this investigation, intelligent predictive stochastic computing is presented by exploitation of artificial neural networks Levenberg-Marquardt approach (ANNs-LMA) to analyze the dynamics of a nonlinear differential delay computer virus (DCV) model. The governing differential delay system with four classes representation with nonlinear delayed ordinary differential equations comprising of uninfected computers, latently infected computers, breaking out computers and computers having antivirus ability. The Adams approach for numerical solution is applied to produce the reference dataset by the variation in recruiting and detaching rate for both old as well as new PCs, bilinear transmission rate amongst healthy versus latently infected PCs, rate of latently infected PCs that break out, the rate for which breaking out PCs obtain antiviral ability, the rate for which antimalware PCs defeat all kind of viruses and delay with respect to time. The design ANNs-LMA is utilized to determine the approximate solution of the nonlinear DCV model by arbitrarily dividing the created dataset for training, testing as well as validation samples during learning of the networks. Negligible absolute errors, mean square errors, and relatively close to perfect modeling with regression metrics endorsed the strength, viability and reliability of the design ANNs-LMA for solving the nonlinear DCV models.

The Bland-Altman method should not be used when one of the two measurement methods has negligible measurement errors.

The Bland-Altman limits of agreement (LoA) method is almost universally used to compare two measurement methods when the outcome is continuous, despite warnings regarding the often-violated strong underlying statistical assumptions. In settings where only a single measurement per individual has been performed and one of the two measurement methods is exempt (or almost) from any measurement error, the LoA method provides biased results, whereas this is not the case for linear regression. Thus, our goal is to explain why this happens and illustrate the advantage of linear regression in this particular setting. For our illustration, we used two data sets: a sample of simulated data, where the truth is known, and data from a validation study on the accuracy of a smartphone image-based dietary intake assessment tool. Our results show that when one of the two measurement methods is exempt (or almost) from any measurement errors, the LoA method should not be used as it provides biased results. In contrast, linear regression of the differences on the precise method was unbiased. The LoA method should be abandoned in favor of linear regression when one of the two measurement methods is exempt (or almost) from measurement errors.

On the Failure of the Area Metric for Validation Exercises of Stochastic Simulations

Abstract This paper discusses the application of the area metric to the quantification of modeling errors. The focus of the discussion is the effect of the shape of the two distributions on the result produced by the Area Metric. Two different examples that assume negligible experimental and numerical errors are presented: the first case has experimental and simulated quantities of interest defined by normal distributions that require the definition of a mean value and a standard deviation; the second example is taken from the V&amp;V10.1 ASME standard that applies the Area Metric to quantify the modeling error of the tip deflection of a loaded hollow tapered cantilever beam simulated with the static Bernoulli–Euler beam theory. The first example shows that relatively small differences between the mean values are sufficient for the area metric to be insensitive to the standard deviation. Furthermore, the example of the V&amp;V10.1 ASME standard produces an Area Metric equal to the difference between the mean values of experiments and simulations. Therefore, the error quantification is reduced to a single number that is obtained from a simple difference of two mean values. This means that the area metric fails to reflect a dependence on the difference in the shape of the distributions representing variability. The paper also presents an alternative version of the Area Metric that avoids this filtering effect of the shape of the distributions by utilizing a reference simulation that has the same mean value as the experiments. This means that the quantification of the modeling error will have contributions from the difference in mean values and the shape of the distributions.

Hounsfield Units measured in low dose CT reliably assess vertebral trabecular bone density changes over two years in axial spondyloarthritis

ObjectivesTo describe low dose Computed Tomography (ldCT) Hounsfield Units (HU) two-year change-from-baseline values (expressing trabecular bone density changes) and analyse their inter-reader reliability per vertebra in radiographic axial spondyloarthritis (r-axSpA). MethodsWe used 49 patients with r-axSpA from the multicentre two-year Sensitive Imaging in Ankylosing Spondylitis (SIAS) study. LdCT HU were independently measured by two trained readers at baseline and two years. Mean (standard deviation, SD) for the change-from-baseline HU values were provided per vertebra by reader. Intraclass correlation coefficients (ICC; absolute agreement, two-way random effect), Bland-Altman plots and smallest detectable change (SDC) were obtained. Percentages of vertebrae in which readers agreed on the direction of change and on change >|SDC| were computed. ResultsOverall, 1,053 (98% of all possible) vertebrae were assessed by each reader both at baseline and two years. Over two years, HU mean change values varied from -23 to 28 and 29 for reader 1 and 2, respectively. Inter-reader reliability of the two-year change-from-baseline values per vertebra was excellent: ICC:0.91-0.99; SDC:6-10; Bland-Altman plots were homoscedastic, with negligible systematic error between readers. Readers agreed on the direction of change in 88-96% and on change >|SDC| in 58-94% of vertebrae, per vertebral level, from C3 to L5. Overall, similar results were obtained across all vertebrae. ConclusionLdCT measurement of HU is a reliable method to assess two-year changes in trabecular bone density at each vertebra from C3-L5. Being reliable across all vertebrae, this methodology can aid the study of trabecular bone density changes over time in r-axSpA, a disease affecting the whole spine.

Analisis Keselamatan dan Kesehatan Kerja Menggunakan Metode Hazard Identification And Risk Assessment Pada Proyek Konstruksi

The construction service sector is one of the many business fields that are classified as very prone to accidents. This research takes a case study, namely the Bali Kura-Kura Creative Campus Development Project which requires a lot of human resources and is of course prone to construction accidents due to negligence or human error. The purpose of this study is to determine the level of accident risk that occurs and provide K3 recommendations after knowing the level of accident risk. In this study the questionnaire was used as a data collection tool. Questionnaires were compiled from predetermined research variables. After knowing the variables and data, the next step is to apply the Hazard Identification and Risk Assessment (HIRA) method&#x0D; The conclusion that can be drawn from this research is the calculation using the HIRA method, which is that there are activities with a low risk level of 3 (15%) sources in the excavation area. Then there are 5 sources (25%) that are included in the medium risk level and 10 sources (50%) of the risk that are included in the high risk category found in all activity areas. Meanwhile, in the extreme risk category, there are 2 sources (10%) found in the excavation area and based on the analysis, it can be seen that the recommendations for improvement proposals that can be made include tightening supervision on each worker activity, enforcing SOPs for each activity, providing safety signs on certain areas that may pose a potential hazard, provide personal protective equipment (PPE).