Negligible Error Research Articles

Study of time fractional proportional delayed multi‐pantograph system and integro‐differential equations

This paper is devoted to the implementation of fractional differential transform method (FDTM) for the evaluation of new approximations for the fractional models of the system of multi‐pantograph differential equations. Some new properties of FDTM have also been proposed for the fractional integro‐differential equations. It is noteworthy that the integro‐differential equations are studied for integers in the literature, and we have proposed the solutions of proportional delayed integro‐differential equations for fractional order. The evaluated results are verified numerically by considering some test examples of fractional integro‐differential equations and fractional models of the system of multi‐pantograph differential equations. The outcomes conclude that evaluated series solutions via FDTM promise more appropriate approximations possess very negligible error and represent the convergent approximations. Thus, evaluated series solutions are comparatively more accurate and require less computational time.

DFT-based QM/MM with particle-mesh Ewald for direct, long-range electrostatic embedding.

We present a density functional theory (DFT)-based, quantum mechanics/molecular mechanics (QM/MM) implementation with long-range electrostatic embedding achieved by direct real-space integration of the particle-mesh Ewald (PME) computed electrostatic potential. The key transformation is the interpolation of the electrostatic potential from the PME grid to the DFT quadrature grid from which integrals are easily evaluated utilizing standard DFT machinery. We provide benchmarks of the numerical accuracy with choice of grid size and real-space corrections and demonstrate that good convergence is achieved while introducing nominal computational overhead. Furthermore, the approach requires only small modification to existing software packages as is demonstrated with our implementation in the OpenMM and Psi4 software. After presenting convergence benchmarks, we evaluate the importance of long-range electrostatic embedding in three solute/solvent systems modeled with QM/MM. Water and 1-butyl-3-methylimidazolium tetrafluoroborate (BMIM/BF4) ionic liquid were considered as "simple" and "complex" solvents, respectively, with water and p-phenylenediamine (PPD) solute molecules treated at the QM level of theory. While electrostatic embedding with standard real-space truncation may introduce negligible errors for simple systems such as water solute in water solvent, errors become more significant when QM/MM is applied to complex solvents such as ionic liquids. An extreme example is the electrostatic embedding energy for oxidized PPD in BMIM/BF4 for which real-space truncation produces severe errors even at 2-3nm cutoff distances. This latter example illustrates that utilization of QM/MM to compute redox potentials within concentrated electrolytes/ionic media requires carefully chosen long-range electrostatic embedding algorithms with our presented algorithm providing a general and robust approach.

Mapping-Aware Kernel Partitioning Method for CGRAs Assisted by Deep Learning

Coarse-grained reconfigurable architectures (CGRAs) provide high energy efficiency with word-level programmability rather than bit-level ones such as FPGAs. The coarser reconfigurability brings about higher energy efficiency and reduces the complexity of compiler tasks compared to the FPGAs. However, application mapping process for CGRAs is still time-consuming. When the compiler tries to map a large and complicated application data-flow-graph(DFG) onto the reconfigurable fabric, it tends to result in inefficient resource use or to fail in mapping. In case of failure, the compiler must divide it into several sub-DFGs and goes back to the same flow. In this work, we propose a novel partitioning method based on a genetic algorithm to eliminate the unmappable DFGs and improve the mapping quality. In order not to generate unmappable sub-DFGs, we also propose an estimation model which predicts the mappability and resource requirements using a DGCNN (Deep Graph Convolutional Neural Network). The genetic algorithm with this model can seek the most resource-efficient mapping without the back-end mapping process. Our model can predict the mappability with more than 98% accuracy and resource usage with a negligible error for two studied CGRAs. Besides, the proposed partitioning method demonstrates 53-75% of memory saving, 1.28-1.39x higher throughput, and better mapping quality over three comparative approaches.

Highly sensitive few-layer MoS2 nanosheets as a stable soil moisture and humidity sensor

In this work, an MoS2 nanosheet based ultrasensitive humidity and soil moisture sensor has been developed. MoS2 nanosheets are obtained by sonication based surfactant-induced exfoliation. The performance of the sensor is examined at room temperature (25 ∘C) with respect to various relative humidity (RH) levels and soil water content (θg). A 434 fold variation (35–15200 pF) in capacitance value is seen when the relative humidity level changes from 11% to 96%. The response and recovery times of the microsensor are approximately 30 s and 40 s, respectively. The capacitance of the MoS2 based sensor increases monotonically when the soil moisture content increases from 3.6% to 53% and 2.3–45% for black and red soil, respectively. The observed variation in capacitance corresponding to θgB = 53% (black soil moisture content) and θgR = 45% (red soil moisture content) is approximately 250 and 200 times that observed for the respective dry soil samples. The error in the soil moisture response measurement for the temperature variation from 25 ∘C to 70 ∘C is ± 2.52%. The device features stable performance for six months with reasonably high accuracy (maximum error ± 1.50%). The soil sensing results show fast response (35 s), negligible hysteresis error (2.80% at θgB = 31.9%), long-term stability (6 months and more), and good reproducibility (with more than 10 cycles).

Optimization and Design of College Students’ Innovation and Entrepreneurship System Based on Computational Intelligence

At present, college students’ innovation and entrepreneurship has gradually become a hot topic nowadays, and the popularity of related knowledge of college students’ innovation and entrepreneurship is getting higher and higher, and more college students have joined in independent innovation and entrepreneurship. In order to alleviate a series of problems in college students’ innovation and entrepreneurship, we design an SD model under computational intelligence to put forward corresponding solutions to the problems faced in innovation and entrepreneurship, through the establishment of relevant models and experiments to verify that SD model is based on computational intelligence to optimize the design of college students’ innovation and entrepreneurship system. We know that the dimensions of tools include three dimensions: supply, demand, and environment. With the change of time, college students’ choice of tools for innovation and entrepreneurship has developed from a single supply tool to a multidimensional tool choice. Through the establishment of SD computational intelligence model to optimize the innovation and entrepreneurship system of college students, through the survey data, it can be seen that the prediction error value of SD model is about 9% and less than 10%, which is within the negligible error range, indicating that SD model has an optimization effect on the innovation and entrepreneurship system of college students. According to the survey, the three dimensions of capital investment, experience, and education level have an impact on college students’ innovation and entrepreneurship, accounting for 45%, 25%, and 30%, respectively. By comparing GW model, FIT model, and PBL model with SD model proposed in this paper, the data of precision, recall rate, accuracy rate, and F1 are compared and analyzed under the condition of changing the three dimensions of capital investment, experience, and education level, so as to select the best optimization model of innovation and entrepreneurship system for computational intelligence college students. Experiments show that when the capital investment increases by 10%, the relevant data of SD model is the best, with accuracy of 0.8761, recall rate of 0.9563, accuracy rate of 0.8972, and F1 of 0.8773; When the experience is increased by 10%, the optimization efficiency of SD model is the best, with its accuracy of 0.8660, recall rate of 0.9462, accuracy rate of 0.8871, and F1 of 0.8672. When the education level is increased by 10%, the optimization efficiency of SD model is the best, with accuracy of 0.8921, recall rate of 0.9762, accuracy rate of 0.8762, and F1 of 0.8861.

An Effective DC-Link Voltage Control Strategy for Grid-Connected PMVG-Based Wind Energy Conversion System

This study presents an effective control strategy for regulating the DC-link voltage in a variable-speed direct-driven (DD) wind energy conversion system (WECS) using a permanent magnet vernier generator (PMVG). To do this, at first, the overall system is configured using back-to-back (BTB) voltage source converters, and the whole system’s dynamical equations are modeled and presented. Following that, a non-linear sliding mode control strategy is introduced as the grid-side converter’s DC-link voltage controller to improve the dynamic performance of the PMVG system and achieve the stable power transfer with the grid. To accomplish this, a proportional and integral (PI)-based sliding surface is designed, and a hybrid reaching law is proposed to suppress chattering and deliver a faster response of DC-link voltage with negligible steady-state tracking error. Finally, the effectiveness and superiority of the proposed control strategy are validated through comparisons with existing methods using simulation and experimental results from a 5-kW PMVG system.

Investigation of the Accuracy of Four Intraoral Scanners in Mandibular Full-Arch Digital Implant Impression: A Comparative In Vitro Study.

Background: We compare the accuracy of new intraoral scanners (IOSs) in full-arch digital implant impressions. Methods: A master model with six scan bodies was milled in poly(methyl methacrylate), measured by using a coordinate measuring machine, and scanned 15 times with four IOSs: PrimeScan, Medit i500, Vatech EZ scan, and iTero. The software was developed to identify the position points on each scan body. The 3D position and distance analysis were performed. Results: The average and ± standard deviation of the 3D position analysis was 29 μm ± 6 μm for PrimeScan, 39 μm ± 6 μm for iTero, 48 μm ± 18 μm for Mediti500, and 118 μm ± 24 μm for Vatech EZ scan (p < 0.05). Conclusions: All IOSs are able to make a digital complete implant impression in vitro according to the average misfit value reported in literature (150 μm); however, the 3D distance analysis showed that only the Primescan and iTero presented negligible systematic error sources.

Novel Torque and Drag Model for Drilling Two-Dimensional High-Angle Wells.

Planning high-angle wells involves diverse areas; one of the most important of these areas is torque and drag (T&D) management. Not only could uncontrolled T&D cause various drilling problems like drill string (D/S) failures, casing wear, stuck pipes, and slow rates of penetration but it could also entirely stop the drilling progress, if torque and/or drag exceed rig or string capabilities. Modeling T&D in advance would alleviate these problems by prediction of friction forces to be encountered and urging the drilling team to take the required measures to mitigate these forces or upgrade the drilling hardware (rig equipment and/or D/S). Modeling T&D is still a complex and time-consuming job to be carried out at the rig site while drilling, so that an accurate and rig-friendly model would be very useful to industry. In this work, a novel and simple model had been developed to predict T&D values while drilling both curve and tangent sections of high-angle wells based on a soft-string concept, in which the D/S is assumed to be a chain lying on the lower side of the well that can transmit torsional forces. Despite the simplicity of the calculations, the model accounts for components of drilling torque that are overlooked in most complex packages. Friction within the top drive system had been considered to predict the torque acting on the D/S only. In addition, the torque applied on the D/S by the viscous drilling fluid was accounted for by reversing the concept of viscometers. The model proved to be practical and reliable for the two-dimensional wellbore and thus is superior in terms of quick field application. The developed model was tested using data from the Western Desert, Egypt. Statistical analysis had been used to assure the accuracy of the proposed model and to assess the effect of different drilling parameters and practices on both T&D. The reliability of the model had been proven with a negligible error for drag calculations and 10% error on average for torque calculations. Also, the effect of distance between successive survey stations on T&D modeling had been proven mathematically. This research narrows the gap between theory and practice by studying the dominant factors and determining the extent of the effect of each of them on wellbore friction forces. In addition, the work sheds light on the best practices concluded from the application of the developed model on field data.

Gold Standard in selection of rainfall forecasting models for soybean crops region

Rainfall data forecasting is essential in agricultural sciences due to impacts caused by water excess or deficit on crop growth. Our study aimed to develop a method to select rainfall forecast models using references with negligible error denoted as the gold standard. To this end, we used forecasting models from national centers such as Canadian Meteorological Center (CMC), European Center for Medium-Range Weather Forecasts (ECMWF), National Center for Environmental Prediction (NCEP), and Center for Weather Forecasting and Climate Studies (CPTEC). The study area comprised the western mesoregion of Paraná State (Brazil), and data were gathered from October to March between the soybean crop seasons of 2010/2011 and 2015/2016. Ten-day period clusters, corresponding to 240 h forecasts in the centers, were used to assess agreement with the gold standard. Our results showed that forecasting center selection must be based on rainfall value ranges and geographic locations. Selection according to the highest agreement with the gold standard was estimated at 76.9% for range 1 in CPTEC, 38.5% for range 2 and 4 in ECMWF, and 38.5% for range 3 in NCEP. In conclusion, the proposed method was efficient in selecting forecasting centers in areas of interest

Impact of Process Variables of Acetone Vapor Jet Drilling on Surface Roughness and Circularity of 3D-Printed ABS Parts: Fabrication and Studies on Thermal, Morphological, and Chemical Characterizations.

Ever since the introduction of 3D printing, industries have seen an exponential growth in production and efficiency. Three-dimensional printing is the process of additive manufacturing (AM) in which the conventional method of material removal is challenged. Layer-on-layer deposition is the basic principle of the AM. Additive manufacturing technologies are used to create 3D-printed objects. An object is built in an additive technique by laying down successive layers of material until the object is complete. Each of these layers can be viewed as a cross-section of the item that has been lightly cut. When compared to traditional production methods, 3D printing allows the creation of complicated shapes with less material. In conventional methods, the materials go through several damages due to the tool–workpiece contact creating friction between them and the dissipated heat that damages the material. Overcoming the conventional method of machining with the help of 3D printing is a new advancement in the industries. The process involves using non-conventional methods for the machining of the parts. This research was oriented towards the chemical vapor jet drilling of the acrylonitrile–butadiene–styrene (ABS) materials. ABS materials are highly machinable and can be recycled for further usage. This paper focused on the usage of acetone as the chemical for drilling. The surface roughness and circularity of the drilled hole was taken into account for this research paper. We set up a manual experiment to run tests and get results. A vapor jet machine was designed with acetone as the core for the vapor. Various analyses were also formulated and conducted during experimentations. Surface roughness analysis provided the insight of roughness after the machining with the help of acetone vapor jet spray. SEM and micro-image parameters were also considered for more clear and advanced reports. In this research paper, DSC and FTIR analysis were performed to understand changes in the internal structure and the material properties of the ABS. Moreover, the research aimed to investigate the effect of various inputs processing parameters such as pressure, flow rate, and stand-off distance on the surface roughness and circularity of ABS workpiece material. The Taguchi L9 orthogonal array design was utilized to conduct tests by chemical vapor jet drilling using acetone and to evaluate the performance of the set-up while reducing the influence of interfering factors in order to provide reliable surface finish and circularity results. The results and conclusion of the research paper aimed to determine the most suitable parameters for the non-conventional acetone vapor jet drilling of the ABS material. The theoretical calculations predicted 1.64432 and 0.3289080 values of surface roughness and circularity, respectively. On the other hand, the experimental values were recorded as 1.598 for surface roughness and 0.322 for circularity. Therefore, a negligible error of 0.046 for surface roughness and 0.0031 for circularity, respectively, was noted which validate the statistical equations and the consistency of the combined vapor jet drilling process.

Low-Complexity Sampling Frequency Offset Estimation and Compensation Scheme for OFDM-Based UWOC System

In this paper, a simple sampling frequency offset (SFO) estimation and compensation scheme based on two phase-conjugated pilots is proposed and experimentally demonstrated in an OFDM-based underwater wireless optical communication (UWOC) system. The phase shift is obtained by simple multiplication for phase-conjugated pilots, and the results are averaged to perform more accurate phase estimation. The experimental results show that the estimation offset is limited within ±3 ppm when the SFO ranges from −1000 ppm to +1000 ppm over a 1 m tap water channel. Moreover, with the help of the proposed scheme, up to ±300 ppm SFO can be well-compensated with error vector magnitude (EVM) penalties below 1 dB after 1 m underwater transmission. In addition, the results demonstrate that, compared with the ideal case without SFO, our proposed SFO compensation scheme can provide nearly negligible bit error rate (BER) penalties in saltwater with the highly scattering property.

Periodic Collapsed Solid-Core PCF Based Modal Interferometer for Chemical Sensing

We experimentally demonstrated a periodically collapsed solid-core photonic crystal fiber (SCPCF) based modal interferometer for sensing refractive index (RI) of chemicals. A given piece of SCPCF is periodically collapsed using an easily and widely available commercial splicer machine and a micropositioner. The distance between the collapsed and number of collapsed regions can be controlled precisely by the inexpensive micropositioner. The effect of the number of periods on the RI sensitivity has been studied. Our experimental results show that this simple and easily fabricated interferometer has an RI sensitivity of 230 nm/RIU with negligible cross-temperature error.

OPTIMAL INTERPOLATION OF AERONET RADIOMETRIC NETWORK OBSERVATIONS FOR THE EVALUATION OF THE AEROSOL OPTICAL DEPTH DISTRIBUTION IN THE EASTERN EUROPEAN REGION

The application of the optimal interpolation method for the assimilation of the observational data of aerosol optical thickness (AOT), which are sparse in space and time, in the chemical transport model is herein validated. Assuming the negligible error of AOT observations in the AERONET ground-based radiometric network, spatial and temporal correlation functions of the errors in the results of the AOT simulated by the GEOS-Chem chemical transport model are obtained. The optimal interpolation method is applied to the AERONET data using the GEOS-Chem results as a background field for the Eastern European region for 2015–2016. It is shown that the use of the optimal interpolation method permits to reduce the root-mean-square error of the AOT estimation by more than a third in places where there are no AERONET stations in comparison with the model results.

Climate Modeling in Low Precision: Effects of Both Deterministic and Stochastic Rounding

Abstract Motivated by recent advances in operational weather forecasting, we study the efficacy of low-precision arithmetic for climate simulations. We develop a framework to measure rounding error in a climate model, which provides a stress test for a low-precision version of the model, and we apply our method to a variety of models including the Lorenz system, a shallow water approximation for flow over a ridge, and a coarse-resolution spectral global atmospheric model with simplified parameterizations (SPEEDY). Although double precision [52 significant bits (sbits)] is standard across operational climate models, in our experiments we find that single precision (23 sbits) is more than enough and that as low as half precision (10 sbits) is often sufficient. For example, SPEEDY can be run with 12 sbits across the code with negligible rounding error, and with 10 sbits if minor errors are accepted, amounting to less than 0.1 mm (6 h)−1 for average gridpoint precipitation, for example. Our test is based on the Wasserstein metric and this provides stringent nonparametric bounds on rounding error accounting for annual means as well as extreme weather events. In addition, by testing models using both round-to-nearest (RN) and stochastic rounding (SR) we find that SR can mitigate rounding error across a range of applications, and thus our results also provide some evidence that SR could be relevant to next-generation climate models. Further research is needed to test if our results can be generalized to higher resolutions and alternative numerical schemes. However, the results open a promising avenue toward the use of low-precision hardware for improved climate modeling. Significance Statement Weather and climate models provide vital information for decision-making, and will become ever more important in the future with a changed climate and more extreme weather. A central limitation to improved models are computational resources, which is why some weather forecasters have recently shifted from conventional 64-bit to more efficient 32-bit computations, which can provide equally accurate forecasts. Climate models, however, still compute in 64 bits, and adapting to lower precision requires a detailed analysis of rounding errors. We develop methods to quantify rounding error in a climate model, and find similar precision acceptable across weather and climate models, with even 16 bits often sufficient for an accurate climate. This opens a promising avenue for computational efficiency gains in climate modeling.

Bayesian Estimation of the Parameters of the Odd Generalized Exponentiated - Inverse Exponential Distribution (OGE -IED)

The Odd Generalized Exponentiated-Inverse Exponential Distribution, a three parameter distribution, is a hybrid of the Generalized Exponential distribution. Each of the parameters were assigned a gamma prior independently resulting to a posterior distribution that is mathematically intractable impossible to obtain marginal posterior distribution for two of the parameters, and a likelihood function that is not known traditionally to R or other statistical software. Resort was made to STAN in order to obtain Bayesian estimates - leveraging on STAN’s provision for user-defined distribution functions. Two datasets were used; remission times (in months) of bladder cancer patients and COVID-19 Survey data in Andalusia, Spain. In the end, the Maximum Likelihood estimates maximized the likelihood more than the Bayesian estimates - though with a slight margin of not more than 0.77. On the other hand, the Bayesian estimates proved to be more stable yielding very negligible standard errors compared to the Maximum Likelihood estimates.

A new method to overcome difficulties of measurements for energy and exergy auditing of municipal solid Waste-to-Energy plants

The current study develops the energy auditing method presented in ASME PTC 4.3 and introduces a novel and applicable method to exergy audit the waste-to-energy power plants. The presented method does not require the waste’s chemical analysis, and the heating value of the solid waste (fuel) is calculated using energy and mass balances around several control volumes within the system instead. Moreover, to calculate the chemical exergy of the fuel, based on its HHV, a set of equations are tested, and the best one is chosen that yields the most negligible error. The fuel mass flow is estimated indirectly using an innovated method. The energy and exergy efficiencies are estimated by the indirect method. The different sources of exergetic inefficiency are presented that are defined as nine exergy losses and two exergy destruction elements. Losses are mainly due to the exiting exhaust gas from the stack, exiting residue and leachate, unburnt carbon, and casing heat loss. Irreversibilitties in the gasifier, combustion chamber, and the HRSG is defined as the exergy destructions that contribute to efficiency reduction. Finally, the developed method is applied to the actual data acquired from an existing power plant in Iran. According to the results of the exergy audit, the exergy efficiency of the considered plant is 25.3%. Here, the exergy destruction in the gasifier and combustion chamber and the exergy loss due to the exiting exhaust gas have the most remarkable impact on reducing the efficiency of the system.

Defect measurement limits using flash thermography with application to additive manufacturing

This paper assesses the possibility of measuring small, shallow defects in low thermal diffusivity materials with existing pulse thermography techniques. Defects like these are commonly introduced in additive manufacturing (AM), and their presence can cause inconsistencies in the mechanical properties of the final part. An axisymmetric, numerical model was created to simulate the heat transfer within these low thermal diffusivity structures during flash thermography. Deviations from the ideal conditions commonly used in flash thermography models such as conduction across the flaw and in-depth absorption of the incident pulse were included in the model and their effects on defect measurability were investigated. These nonideal conditions (in addition to free convection) introduce depth measurement errors of >10% even for large aspect ratio defects (such as delaminations). The figures provided quantify the amount of error associated with these individual parameters and can be used to determine if a given thermography system is ideal. The simulation results also demonstrate that traditional 1D thermography models may be used to approximate the depth of a defect with negligible error if the defect has an aspect ratio greater than 6. Smaller defects may also be measured with minimal error. If flash thermography were performed during AM to identify the layers where defects most likely reside, the measurement limits would be even less restrictive.

Joint Optimization of Data Freshness and Fidelity for Selection Combining-Based Transmissions.

Motivated by big data applications in the Internet of Things (IoT), abundant information arrives at the fusion center (FC) waiting to be processed. It is of great significance to ensure data freshness and fidelity simultaneously. We consider a wireless sensor network (WSN) where several sensor nodes observe one metric and then transmit the observations to the FC using a selection combining (SC) scheme. We adopt the age of information (AoI) and minimum mean square error (MMSE) metrics to measure the data freshness and fidelity, respectively. Explicit expressions of average AoI and MMSE are derived. After that, we jointly optimize the two metrics by adjusting the number of sensor nodes. A closed-form sub-optimal number of sensor nodes is proposed to achieve the best freshness and fidelity tradeoff with negligible errors. Numerical results show that using the proposed node number designs can effectively improve the freshness and fidelity of the transmitted data.

Contribution to the service-life modeling of concrete exposed to sulfate attack by the inclusion of electrical resistivity data

The objective of this study was to explore the modeling of the external sulfate attack (ESA) using electrical resistivity data as an input parameter of the equations. An extensive experimental program was designed to relate the sulfate diffusion coefficients to the electrical resistivity of concrete. For this purpose, concrete specimens were cast and exposed to wetting and drying cycles in sodium sulfate solutions for 140 days. Sulfate profiles were determined after 28, 56, 112, and 140 days of exposure. The diffusion coefficient was determined by Fick’s law of diffusion. The well-known Einstein relation between the diffusion coefficient and the electrical resistivity was tested and two existing service-life models from the literature were adjusted with the inclusion of electrical resistivity to predict the entry of sulfates into concrete. Results indicated a negligible prediction error varying around 7%.

High-resolution on-chip Fourier transform spectrometer based on cascaded optical switches.

Chip-level spectrometers provide a stable and cost-effective solution for spectral analysis in various applications. Here we present a silicon on-chip digital Fourier transform spectrometer consisting of eight cascaded optical switches connected by delay waveguides. By configuring the states of the optical switches, this chip can realize 127 Mach-Zehnder interferometers with linearly increased optical path differences. A machine-learning regularization method is utilized to reconstruct the spectrum. Experimental results show that our chip can retrieve both sparse and broadband optical spectra with negligible reconstruction errors. The spectral resolution can be further improved by cascading more stages of optical switches. Our method has the advantages of compact size, high scalability, and high signal-to-noise ratio, making it a promising candidate for realizing miniaturized spectrometers.